Overview

Dataset statistics

Number of variables90
Number of observations3991
Missing cells274918
Missing cells (%)76.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory720.0 B

Variable types

Categorical39
Boolean34
Unsupported8
Numeric9

Alerts

bad_behaviour_card has constant value ""Constant
ball_receipt_outcome has constant value ""Constant
ball_recovery_offensive has constant value ""Constant
ball_recovery_recovery_failure has constant value ""Constant
block_deflection has constant value ""Constant
block_offensive has constant value ""Constant
clearance_aerial_won has constant value ""Constant
clearance_head has constant value ""Constant
clearance_left_foot has constant value ""Constant
clearance_right_foot has constant value ""Constant
counterpress has constant value ""Constant
dribble_nutmeg has constant value ""Constant
dribble_overrun has constant value ""Constant
foul_committed_advantage has constant value ""Constant
foul_committed_offensive has constant value ""Constant
foul_won_advantage has constant value ""Constant
foul_won_defensive has constant value ""Constant
match_id has constant value ""Constant
miscontrol_aerial_won has constant value ""Constant
off_camera has constant value ""Constant
out has constant value ""Constant
pass_aerial_won has constant value ""Constant
pass_cross has constant value ""Constant
pass_deflected has constant value ""Constant
pass_goal_assist has constant value ""Constant
pass_inswinging has constant value ""Constant
pass_miscommunication has constant value ""Constant
pass_no_touch has constant value ""Constant
pass_outswinging has constant value ""Constant
pass_shot_assist has constant value ""Constant
pass_straight has constant value ""Constant
pass_switch has constant value ""Constant
pass_through_ball has constant value ""Constant
shot_aerial_won has constant value ""Constant
shot_deflected has constant value ""Constant
shot_first_time has constant value ""Constant
under_pressure has constant value ""Constant
id has a high cardinality: 3991 distinct valuesHigh cardinality
timestamp has a high cardinality: 2776 distinct valuesHigh cardinality
duration is highly overall correlated with pass_length and 13 other fieldsHigh correlation
df_index is highly overall correlated with minute and 8 other fieldsHigh correlation
minute is highly overall correlated with df_index and 8 other fieldsHigh correlation
pass_angle is highly overall correlated with pass_assisted_shot_id and 2 other fieldsHigh correlation
pass_length is highly overall correlated with duration and 2 other fieldsHigh correlation
player_id is highly overall correlated with pass_assisted_shot_id and 5 other fieldsHigh correlation
possession is highly overall correlated with df_index and 8 other fieldsHigh correlation
second is highly overall correlated with foul_committed_card and 6 other fieldsHigh correlation
shot_statsbomb_xg is highly overall correlated with shot_key_pass_id and 2 other fieldsHigh correlation
clearance_body_part is highly overall correlated with duration and 1 other fieldsHigh correlation
dribble_outcome is highly overall correlated with typeHigh correlation
duel_outcome is highly overall correlated with duel_type and 1 other fieldsHigh correlation
duel_type is highly overall correlated with duel_outcome and 2 other fieldsHigh correlation
foul_committed_card is highly overall correlated with duration and 2 other fieldsHigh correlation
foul_committed_type is highly overall correlated with duration and 6 other fieldsHigh correlation
goalkeeper_body_part is highly overall correlated with duration and 7 other fieldsHigh correlation
goalkeeper_outcome is highly overall correlated with duration and 3 other fieldsHigh correlation
goalkeeper_position is highly overall correlated with duration and 2 other fieldsHigh correlation
goalkeeper_technique is highly overall correlated with duration and 4 other fieldsHigh correlation
goalkeeper_type is highly overall correlated with duration and 3 other fieldsHigh correlation
interception_outcome is highly overall correlated with duration and 3 other fieldsHigh correlation
pass_assisted_shot_id is highly overall correlated with duration and 20 other fieldsHigh correlation
pass_body_part is highly overall correlated with pass_assisted_shot_id and 1 other fieldsHigh correlation
pass_height is highly overall correlated with pass_assisted_shot_id and 1 other fieldsHigh correlation
pass_outcome is highly overall correlated with pass_technique and 1 other fieldsHigh correlation
pass_recipient is highly overall correlated with pass_assisted_shot_id and 5 other fieldsHigh correlation
pass_technique is highly overall correlated with pass_angle and 6 other fieldsHigh correlation
pass_type is highly overall correlated with pass_assisted_shot_id and 4 other fieldsHigh correlation
period is highly overall correlated with df_index and 7 other fieldsHigh correlation
play_pattern is highly overall correlated with duel_type and 5 other fieldsHigh correlation
player is highly overall correlated with player_id and 9 other fieldsHigh correlation
position is highly overall correlated with player_id and 13 other fieldsHigh correlation
possession_team is highly overall correlated with interception_outcome and 8 other fieldsHigh correlation
possession_team_id is highly overall correlated with interception_outcome and 8 other fieldsHigh correlation
shot_body_part is highly overall correlated with shot_key_pass_id and 1 other fieldsHigh correlation
shot_key_pass_id is highly overall correlated with duration and 18 other fieldsHigh correlation
shot_outcome is highly overall correlated with shot_key_pass_id and 1 other fieldsHigh correlation
shot_technique is highly overall correlated with shot_statsbomb_xg and 2 other fieldsHigh correlation
shot_type is highly overall correlated with play_pattern and 2 other fieldsHigh correlation
substitution_outcome is highly overall correlated with duration and 6 other fieldsHigh correlation
substitution_replacement is highly overall correlated with duration and 14 other fieldsHigh correlation
team is highly overall correlated with player_id and 9 other fieldsHigh correlation
type is highly overall correlated with pass_angle and 28 other fieldsHigh correlation
goalkeeper_position is highly imbalanced (57.8%)Imbalance
pass_body_part is highly imbalanced (58.6%)Imbalance
pass_outcome is highly imbalanced (64.4%)Imbalance
substitution_outcome is highly imbalanced (53.1%)Imbalance
bad_behaviour_card has 3989 (99.9%) missing valuesMissing
ball_receipt_outcome has 3883 (97.3%) missing valuesMissing
ball_recovery_offensive has 3990 (> 99.9%) missing valuesMissing
ball_recovery_recovery_failure has 3986 (99.9%) missing valuesMissing
block_deflection has 3990 (> 99.9%) missing valuesMissing
block_offensive has 3989 (99.9%) missing valuesMissing
carry_end_location has 3020 (75.7%) missing valuesMissing
clearance_aerial_won has 3987 (99.9%) missing valuesMissing
clearance_body_part has 3959 (99.2%) missing valuesMissing
clearance_head has 3980 (99.7%) missing valuesMissing
clearance_left_foot has 3984 (99.8%) missing valuesMissing
clearance_right_foot has 3977 (99.6%) missing valuesMissing
counterpress has 3881 (97.2%) missing valuesMissing
dribble_nutmeg has 3989 (99.9%) missing valuesMissing
dribble_outcome has 3950 (99.0%) missing valuesMissing
dribble_overrun has 3989 (99.9%) missing valuesMissing
duel_outcome has 3958 (99.2%) missing valuesMissing
duel_type has 3940 (98.7%) missing valuesMissing
duration has 1043 (26.1%) missing valuesMissing
foul_committed_advantage has 3989 (99.9%) missing valuesMissing
foul_committed_card has 3986 (99.9%) missing valuesMissing
foul_committed_offensive has 3990 (> 99.9%) missing valuesMissing
foul_committed_type has 3988 (99.9%) missing valuesMissing
foul_won_advantage has 3989 (99.9%) missing valuesMissing
foul_won_defensive has 3988 (99.9%) missing valuesMissing
goalkeeper_body_part has 3986 (99.9%) missing valuesMissing
goalkeeper_end_location has 3966 (99.4%) missing valuesMissing
goalkeeper_outcome has 3981 (99.7%) missing valuesMissing
goalkeeper_position has 3959 (99.2%) missing valuesMissing
goalkeeper_technique has 3984 (99.8%) missing valuesMissing
goalkeeper_type has 3956 (99.1%) missing valuesMissing
interception_outcome has 3971 (99.5%) missing valuesMissing
miscontrol_aerial_won has 3990 (> 99.9%) missing valuesMissing
off_camera has 3944 (98.8%) missing valuesMissing
out has 3956 (99.1%) missing valuesMissing
pass_aerial_won has 3979 (99.7%) missing valuesMissing
pass_angle has 2888 (72.4%) missing valuesMissing
pass_assisted_shot_id has 3971 (99.5%) missing valuesMissing
pass_body_part has 2941 (73.7%) missing valuesMissing
pass_cross has 3969 (99.4%) missing valuesMissing
pass_deflected has 3990 (> 99.9%) missing valuesMissing
pass_end_location has 2888 (72.4%) missing valuesMissing
pass_goal_assist has 3989 (99.9%) missing valuesMissing
pass_height has 2888 (72.4%) missing valuesMissing
pass_inswinging has 3989 (99.9%) missing valuesMissing
pass_length has 2888 (72.4%) missing valuesMissing
pass_miscommunication has 3989 (99.9%) missing valuesMissing
pass_no_touch has 3987 (99.9%) missing valuesMissing
pass_outcome has 3823 (95.8%) missing valuesMissing
pass_outswinging has 3989 (99.9%) missing valuesMissing
pass_recipient has 2948 (73.9%) missing valuesMissing
pass_shot_assist has 3973 (99.5%) missing valuesMissing
pass_straight has 3987 (99.9%) missing valuesMissing
pass_switch has 3972 (99.5%) missing valuesMissing
pass_technique has 3971 (99.5%) missing valuesMissing
pass_through_ball has 3979 (99.7%) missing valuesMissing
pass_type has 3824 (95.8%) missing valuesMissing
related_events has 135 (3.4%) missing valuesMissing
shot_aerial_won has 3990 (> 99.9%) missing valuesMissing
shot_body_part has 3959 (99.2%) missing valuesMissing
shot_deflected has 3990 (> 99.9%) missing valuesMissing
shot_end_location has 3959 (99.2%) missing valuesMissing
shot_first_time has 3977 (99.6%) missing valuesMissing
shot_freeze_frame has 3959 (99.2%) missing valuesMissing
shot_key_pass_id has 3971 (99.5%) missing valuesMissing
shot_outcome has 3959 (99.2%) missing valuesMissing
shot_statsbomb_xg has 3959 (99.2%) missing valuesMissing
shot_technique has 3959 (99.2%) missing valuesMissing
shot_type has 3959 (99.2%) missing valuesMissing
substitution_outcome has 3981 (99.7%) missing valuesMissing
substitution_replacement has 3981 (99.7%) missing valuesMissing
tactics has 3987 (99.9%) missing valuesMissing
under_pressure has 3203 (80.3%) missing valuesMissing
id is uniformly distributedUniform
df_index is uniformly distributedUniform
pass_assisted_shot_id is uniformly distributedUniform
shot_key_pass_id is uniformly distributedUniform
substitution_replacement is uniformly distributedUniform
timestamp is uniformly distributedUniform
id has unique valuesUnique
df_index has unique valuesUnique
carry_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
goalkeeper_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
location is an unsupported type, check if it needs cleaning or further analysisUnsupported
pass_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
related_events is an unsupported type, check if it needs cleaning or further analysisUnsupported
shot_end_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
shot_freeze_frame is an unsupported type, check if it needs cleaning or further analysisUnsupported
tactics is an unsupported type, check if it needs cleaning or further analysisUnsupported
duration has 478 (12.0%) zerosZeros
minute has 94 (2.4%) zerosZeros
second has 67 (1.7%) zerosZeros

Reproduction

Analysis started2023-05-19 16:55:39.434228
Analysis finished2023-05-19 16:56:28.851882
Duration49.42 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

bad_behaviour_card
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
Yellow Card
2 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters22
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYellow Card
2nd rowYellow Card

Common Values

ValueCountFrequency (%)
Yellow Card 2
 
0.1%
(Missing) 3989
99.9%

Length

2023-05-19T13:56:28.942920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:29.163408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yellow 2
50.0%
card 2
50.0%

Most occurring characters

ValueCountFrequency (%)
l 4
18.2%
Y 2
9.1%
e 2
9.1%
o 2
9.1%
w 2
9.1%
2
9.1%
C 2
9.1%
a 2
9.1%
r 2
9.1%
d 2
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16
72.7%
Uppercase Letter 4
 
18.2%
Space Separator 2
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 4
25.0%
e 2
12.5%
o 2
12.5%
w 2
12.5%
a 2
12.5%
r 2
12.5%
d 2
12.5%
Uppercase Letter
ValueCountFrequency (%)
Y 2
50.0%
C 2
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20
90.9%
Common 2
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 4
20.0%
Y 2
10.0%
e 2
10.0%
o 2
10.0%
w 2
10.0%
C 2
10.0%
a 2
10.0%
r 2
10.0%
d 2
10.0%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 4
18.2%
Y 2
9.1%
e 2
9.1%
o 2
9.1%
w 2
9.1%
2
9.1%
C 2
9.1%
a 2
9.1%
r 2
9.1%
d 2
9.1%

ball_receipt_outcome
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing3883
Missing (%)97.3%
Memory size31.3 KiB
Incomplete
108 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1080
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIncomplete
2nd rowIncomplete
3rd rowIncomplete
4th rowIncomplete
5th rowIncomplete

Common Values

ValueCountFrequency (%)
Incomplete 108
 
2.7%
(Missing) 3883
97.3%

Length

2023-05-19T13:56:29.338786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:29.553136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
incomplete 108
100.0%

Most occurring characters

ValueCountFrequency (%)
e 216
20.0%
I 108
10.0%
n 108
10.0%
c 108
10.0%
o 108
10.0%
m 108
10.0%
p 108
10.0%
l 108
10.0%
t 108
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 972
90.0%
Uppercase Letter 108
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 216
22.2%
n 108
11.1%
c 108
11.1%
o 108
11.1%
m 108
11.1%
p 108
11.1%
l 108
11.1%
t 108
11.1%
Uppercase Letter
ValueCountFrequency (%)
I 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1080
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 216
20.0%
I 108
10.0%
n 108
10.0%
c 108
10.0%
o 108
10.0%
m 108
10.0%
p 108
10.0%
l 108
10.0%
t 108
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 216
20.0%
I 108
10.0%
n 108
10.0%
c 108
10.0%
o 108
10.0%
m 108
10.0%
p 108
10.0%
l 108
10.0%
t 108
10.0%

ball_recovery_offensive
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3990
Missing (%)> 99.9%
Memory size31.3 KiB
True
 
1
(Missing)
3990 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 3990
> 99.9%
2023-05-19T13:56:29.709371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

ball_recovery_recovery_failure
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing3986
Missing (%)99.9%
Memory size31.3 KiB
True
 
5
(Missing)
3986 
ValueCountFrequency (%)
True 5
 
0.1%
(Missing) 3986
99.9%
2023-05-19T13:56:29.929834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

block_deflection
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3990
Missing (%)> 99.9%
Memory size31.3 KiB
True
 
1
(Missing)
3990 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 3990
> 99.9%
2023-05-19T13:56:30.067754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

block_offensive
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:30.196634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

carry_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3020
Missing (%)75.7%
Memory size31.3 KiB

clearance_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing3987
Missing (%)99.9%
Memory size31.3 KiB
True
 
4
(Missing)
3987 
ValueCountFrequency (%)
True 4
 
0.1%
(Missing) 3987
99.9%
2023-05-19T13:56:30.402599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

clearance_body_part
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)9.4%
Missing3959
Missing (%)99.2%
Memory size31.3 KiB
Right Foot
14 
Head
11 
Left Foot
7 

Length

Max length10
Median length9
Mean length7.71875
Min length4

Characters and Unicode

Total characters247
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRight Foot
2nd rowHead
3rd rowHead
4th rowHead
5th rowRight Foot

Common Values

ValueCountFrequency (%)
Right Foot 14
 
0.4%
Head 11
 
0.3%
Left Foot 7
 
0.2%
(Missing) 3959
99.2%

Length

2023-05-19T13:56:30.602893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:30.945498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
foot 21
39.6%
right 14
26.4%
head 11
20.8%
left 7
 
13.2%

Most occurring characters

ValueCountFrequency (%)
t 42
17.0%
o 42
17.0%
21
8.5%
F 21
8.5%
e 18
7.3%
R 14
 
5.7%
i 14
 
5.7%
g 14
 
5.7%
h 14
 
5.7%
H 11
 
4.5%
Other values (4) 36
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 173
70.0%
Uppercase Letter 53
 
21.5%
Space Separator 21
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 42
24.3%
o 42
24.3%
e 18
10.4%
i 14
 
8.1%
g 14
 
8.1%
h 14
 
8.1%
a 11
 
6.4%
d 11
 
6.4%
f 7
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
F 21
39.6%
R 14
26.4%
H 11
20.8%
L 7
 
13.2%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 226
91.5%
Common 21
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 42
18.6%
o 42
18.6%
F 21
9.3%
e 18
8.0%
R 14
 
6.2%
i 14
 
6.2%
g 14
 
6.2%
h 14
 
6.2%
H 11
 
4.9%
a 11
 
4.9%
Other values (3) 25
11.1%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 42
17.0%
o 42
17.0%
21
8.5%
F 21
8.5%
e 18
7.3%
R 14
 
5.7%
i 14
 
5.7%
g 14
 
5.7%
h 14
 
5.7%
H 11
 
4.5%
Other values (4) 36
14.6%

clearance_head
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)9.1%
Missing3980
Missing (%)99.7%
Memory size31.3 KiB
True
 
11
(Missing)
3980 
ValueCountFrequency (%)
True 11
 
0.3%
(Missing) 3980
99.7%
2023-05-19T13:56:31.285046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

clearance_left_foot
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)14.3%
Missing3984
Missing (%)99.8%
Memory size31.3 KiB
True
 
7
(Missing)
3984 
ValueCountFrequency (%)
True 7
 
0.2%
(Missing) 3984
99.8%
2023-05-19T13:56:31.422153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

clearance_right_foot
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)7.1%
Missing3977
Missing (%)99.6%
Memory size31.3 KiB
True
 
14
(Missing)
3977 
ValueCountFrequency (%)
True 14
 
0.4%
(Missing) 3977
99.6%
2023-05-19T13:56:31.585809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

counterpress
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing3881
Missing (%)97.2%
Memory size31.3 KiB
True
 
110
(Missing)
3881 
ValueCountFrequency (%)
True 110
 
2.8%
(Missing) 3881
97.2%
2023-05-19T13:56:31.728727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

dribble_nutmeg
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:31.963652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

dribble_outcome
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)4.9%
Missing3950
Missing (%)99.0%
Memory size31.3 KiB
Complete
28 
Incomplete
13 

Length

Max length10
Median length8
Mean length8.6341463
Min length8

Characters and Unicode

Total characters354
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowIncomplete
3rd rowComplete
4th rowIncomplete
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete 28
 
0.7%
Incomplete 13
 
0.3%
(Missing) 3950
99.0%

Length

2023-05-19T13:56:32.169974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:32.451065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
complete 28
68.3%
incomplete 13
31.7%

Most occurring characters

ValueCountFrequency (%)
e 82
23.2%
o 41
11.6%
m 41
11.6%
p 41
11.6%
l 41
11.6%
t 41
11.6%
C 28
 
7.9%
I 13
 
3.7%
n 13
 
3.7%
c 13
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 313
88.4%
Uppercase Letter 41
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 82
26.2%
o 41
13.1%
m 41
13.1%
p 41
13.1%
l 41
13.1%
t 41
13.1%
n 13
 
4.2%
c 13
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
C 28
68.3%
I 13
31.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 354
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 82
23.2%
o 41
11.6%
m 41
11.6%
p 41
11.6%
l 41
11.6%
t 41
11.6%
C 28
 
7.9%
I 13
 
3.7%
n 13
 
3.7%
c 13
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 82
23.2%
o 41
11.6%
m 41
11.6%
p 41
11.6%
l 41
11.6%
t 41
11.6%
C 28
 
7.9%
I 13
 
3.7%
n 13
 
3.7%
c 13
 
3.7%

dribble_overrun
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:32.655585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

duel_outcome
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)12.1%
Missing3958
Missing (%)99.2%
Memory size31.3 KiB
Success In Play
17 
Lost In Play
8 
Won
6 
Lost Out
2 

Length

Max length15
Median length15
Mean length11.666667
Min length3

Characters and Unicode

Total characters385
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLost In Play
2nd rowWon
3rd rowSuccess In Play
4th rowSuccess In Play
5th rowSuccess In Play

Common Values

ValueCountFrequency (%)
Success In Play 17
 
0.4%
Lost In Play 8
 
0.2%
Won 6
 
0.2%
Lost Out 2
 
0.1%
(Missing) 3958
99.2%

Length

2023-05-19T13:56:32.804373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:32.995007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
in 25
29.4%
play 25
29.4%
success 17
20.0%
lost 10
 
11.8%
won 6
 
7.1%
out 2
 
2.4%

Most occurring characters

ValueCountFrequency (%)
52
13.5%
s 44
11.4%
c 34
8.8%
n 31
 
8.1%
P 25
 
6.5%
a 25
 
6.5%
y 25
 
6.5%
I 25
 
6.5%
l 25
 
6.5%
u 19
 
4.9%
Other values (7) 80
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 248
64.4%
Uppercase Letter 85
 
22.1%
Space Separator 52
 
13.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 44
17.7%
c 34
13.7%
n 31
12.5%
a 25
10.1%
y 25
10.1%
l 25
10.1%
u 19
7.7%
e 17
 
6.9%
o 16
 
6.5%
t 12
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P 25
29.4%
I 25
29.4%
S 17
20.0%
L 10
 
11.8%
W 6
 
7.1%
O 2
 
2.4%
Space Separator
ValueCountFrequency (%)
52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 333
86.5%
Common 52
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 44
13.2%
c 34
10.2%
n 31
9.3%
P 25
 
7.5%
a 25
 
7.5%
y 25
 
7.5%
I 25
 
7.5%
l 25
 
7.5%
u 19
 
5.7%
S 17
 
5.1%
Other values (6) 63
18.9%
Common
ValueCountFrequency (%)
52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52
13.5%
s 44
11.4%
c 34
8.8%
n 31
 
8.1%
P 25
 
6.5%
a 25
 
6.5%
y 25
 
6.5%
I 25
 
6.5%
l 25
 
6.5%
u 19
 
4.9%
Other values (7) 80
20.8%

duel_type
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)3.9%
Missing3940
Missing (%)98.7%
Memory size31.3 KiB
Tackle
33 
Aerial Lost
18 

Length

Max length11
Median length6
Mean length7.7647059
Min length6

Characters and Unicode

Total characters396
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTackle
2nd rowTackle
3rd rowTackle
4th rowAerial Lost
5th rowAerial Lost

Common Values

ValueCountFrequency (%)
Tackle 33
 
0.8%
Aerial Lost 18
 
0.5%
(Missing) 3940
98.7%

Length

2023-05-19T13:56:33.167768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:33.456066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tackle 33
47.8%
aerial 18
26.1%
lost 18
26.1%

Most occurring characters

ValueCountFrequency (%)
a 51
12.9%
l 51
12.9%
e 51
12.9%
T 33
8.3%
c 33
8.3%
k 33
8.3%
A 18
 
4.5%
r 18
 
4.5%
i 18
 
4.5%
18
 
4.5%
Other values (4) 72
18.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 309
78.0%
Uppercase Letter 69
 
17.4%
Space Separator 18
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 51
16.5%
l 51
16.5%
e 51
16.5%
c 33
10.7%
k 33
10.7%
r 18
 
5.8%
i 18
 
5.8%
o 18
 
5.8%
s 18
 
5.8%
t 18
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
T 33
47.8%
A 18
26.1%
L 18
26.1%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378
95.5%
Common 18
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 51
13.5%
l 51
13.5%
e 51
13.5%
T 33
8.7%
c 33
8.7%
k 33
8.7%
A 18
 
4.8%
r 18
 
4.8%
i 18
 
4.8%
L 18
 
4.8%
Other values (3) 54
14.3%
Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 51
12.9%
l 51
12.9%
e 51
12.9%
T 33
8.3%
c 33
8.3%
k 33
8.3%
A 18
 
4.5%
r 18
 
4.5%
i 18
 
4.5%
18
 
4.5%
Other values (4) 72
18.2%

duration
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct2402
Distinct (%)81.5%
Missing1043
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean1.117904
Minimum0
Maximum14.499599
Zeros478
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:33.695102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.375825
median0.915147
Q31.4731095
95-th percentile3.1049404
Maximum14.499599
Range14.499599
Interquartile range (IQR)1.0972845

Descriptive statistics

Standard deviation1.1588586
Coefficient of variation (CV)1.0366352
Kurtosis19.189927
Mean1.117904
Median Absolute Deviation (MAD)0.5546855
Skewness3.0839218
Sum3295.5811
Variance1.3429533
MonotonicityNot monotonic
2023-05-19T13:56:33.991120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 478
 
12.0%
0.04 25
 
0.6%
0.040000003 14
 
0.4%
0.08 14
 
0.4%
0.079999 6
 
0.2%
0.039999 5
 
0.1%
0.12 5
 
0.1%
1.122397 2
 
0.1%
0.119999 2
 
0.1%
0.593073 2
 
0.1%
Other values (2392) 2395
60.0%
(Missing) 1043
26.1%
ValueCountFrequency (%)
0 478
12.0%
0.001276 1
 
< 0.1%
0.004734 1
 
< 0.1%
0.009418 1
 
< 0.1%
0.022814002 1
 
< 0.1%
0.024965 1
 
< 0.1%
0.03112 1
 
< 0.1%
0.03876 1
 
< 0.1%
0.039999 5
 
0.1%
0.04 25
 
0.6%
ValueCountFrequency (%)
14.499599 1
< 0.1%
11.925499 1
< 0.1%
11.170376 1
< 0.1%
11.125323 1
< 0.1%
10.371027 1
< 0.1%
10.027738 1
< 0.1%
9.521647 1
< 0.1%
9.245178 1
< 0.1%
8.449158 1
< 0.1%
8.331481 1
< 0.1%

foul_committed_advantage
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:34.343848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_committed_card
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)40.0%
Missing3986
Missing (%)99.9%
Memory size31.3 KiB
Yellow Card
4 
Second Yellow
1 

Length

Max length13
Median length11
Mean length11.4
Min length11

Characters and Unicode

Total characters57
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)20.0%

Sample

1st rowYellow Card
2nd rowYellow Card
3rd rowYellow Card
4th rowYellow Card
5th rowSecond Yellow

Common Values

ValueCountFrequency (%)
Yellow Card 4
 
0.1%
Second Yellow 1
 
< 0.1%
(Missing) 3986
99.9%

Length

2023-05-19T13:56:34.521864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:34.700651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yellow 5
50.0%
card 4
40.0%
second 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
l 10
17.5%
e 6
10.5%
o 6
10.5%
Y 5
8.8%
w 5
8.8%
5
8.8%
d 5
8.8%
C 4
 
7.0%
a 4
 
7.0%
r 4
 
7.0%
Other values (3) 3
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42
73.7%
Uppercase Letter 10
 
17.5%
Space Separator 5
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 10
23.8%
e 6
14.3%
o 6
14.3%
w 5
11.9%
d 5
11.9%
a 4
 
9.5%
r 4
 
9.5%
c 1
 
2.4%
n 1
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
Y 5
50.0%
C 4
40.0%
S 1
 
10.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52
91.2%
Common 5
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 10
19.2%
e 6
11.5%
o 6
11.5%
Y 5
9.6%
w 5
9.6%
d 5
9.6%
C 4
 
7.7%
a 4
 
7.7%
r 4
 
7.7%
S 1
 
1.9%
Other values (2) 2
 
3.8%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 10
17.5%
e 6
10.5%
o 6
10.5%
Y 5
8.8%
w 5
8.8%
5
8.8%
d 5
8.8%
C 4
 
7.0%
a 4
 
7.0%
r 4
 
7.0%
Other values (3) 3
 
5.3%

foul_committed_offensive
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3990
Missing (%)> 99.9%
Memory size31.3 KiB
True
 
1
(Missing)
3990 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 3990
> 99.9%
2023-05-19T13:56:34.851111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_committed_type
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)66.7%
Missing3988
Missing (%)99.9%
Memory size31.3 KiB
Dangerous Play
2 
Handball
1 

Length

Max length14
Median length14
Mean length12
Min length8

Characters and Unicode

Total characters36
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)33.3%

Sample

1st rowHandball
2nd rowDangerous Play
3rd rowDangerous Play

Common Values

ValueCountFrequency (%)
Dangerous Play 2
 
0.1%
Handball 1
 
< 0.1%
(Missing) 3988
99.9%

Length

2023-05-19T13:56:35.008203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:35.273300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
dangerous 2
40.0%
play 2
40.0%
handball 1
20.0%

Most occurring characters

ValueCountFrequency (%)
a 6
16.7%
l 4
11.1%
n 3
 
8.3%
D 2
 
5.6%
g 2
 
5.6%
e 2
 
5.6%
r 2
 
5.6%
o 2
 
5.6%
u 2
 
5.6%
s 2
 
5.6%
Other values (6) 9
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29
80.6%
Uppercase Letter 5
 
13.9%
Space Separator 2
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6
20.7%
l 4
13.8%
n 3
10.3%
g 2
 
6.9%
e 2
 
6.9%
r 2
 
6.9%
o 2
 
6.9%
u 2
 
6.9%
s 2
 
6.9%
y 2
 
6.9%
Other values (2) 2
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
D 2
40.0%
P 2
40.0%
H 1
20.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34
94.4%
Common 2
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6
17.6%
l 4
11.8%
n 3
8.8%
D 2
 
5.9%
g 2
 
5.9%
e 2
 
5.9%
r 2
 
5.9%
o 2
 
5.9%
u 2
 
5.9%
s 2
 
5.9%
Other values (5) 7
20.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6
16.7%
l 4
11.1%
n 3
 
8.3%
D 2
 
5.6%
g 2
 
5.6%
e 2
 
5.6%
r 2
 
5.6%
o 2
 
5.6%
u 2
 
5.6%
s 2
 
5.6%
Other values (6) 9
25.0%

foul_won_advantage
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:35.469376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

foul_won_defensive
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing3988
Missing (%)99.9%
Memory size31.3 KiB
True
 
3
(Missing)
3988 
ValueCountFrequency (%)
True 3
 
0.1%
(Missing) 3988
99.9%
2023-05-19T13:56:35.617073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

goalkeeper_body_part
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)60.0%
Missing3986
Missing (%)99.9%
Memory size31.3 KiB
Both Hands
3 
Left Hand
1 
Right Foot
1 

Length

Max length10
Median length10
Mean length9.8
Min length9

Characters and Unicode

Total characters49
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)40.0%

Sample

1st rowLeft Hand
2nd rowBoth Hands
3rd rowRight Foot
4th rowBoth Hands
5th rowBoth Hands

Common Values

ValueCountFrequency (%)
Both Hands 3
 
0.1%
Left Hand 1
 
< 0.1%
Right Foot 1
 
< 0.1%
(Missing) 3986
99.9%

Length

2023-05-19T13:56:35.758720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:35.998560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
both 3
30.0%
hands 3
30.0%
left 1
 
10.0%
hand 1
 
10.0%
right 1
 
10.0%
foot 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
t 6
12.2%
5
10.2%
o 5
10.2%
d 4
8.2%
h 4
8.2%
H 4
8.2%
a 4
8.2%
n 4
8.2%
s 3
6.1%
B 3
6.1%
Other values (7) 7
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34
69.4%
Uppercase Letter 10
 
20.4%
Space Separator 5
 
10.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 6
17.6%
o 5
14.7%
d 4
11.8%
h 4
11.8%
a 4
11.8%
n 4
11.8%
s 3
8.8%
e 1
 
2.9%
f 1
 
2.9%
i 1
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
H 4
40.0%
B 3
30.0%
L 1
 
10.0%
R 1
 
10.0%
F 1
 
10.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44
89.8%
Common 5
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 6
13.6%
o 5
11.4%
d 4
9.1%
h 4
9.1%
H 4
9.1%
a 4
9.1%
n 4
9.1%
s 3
6.8%
B 3
6.8%
L 1
 
2.3%
Other values (6) 6
13.6%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 6
12.2%
5
10.2%
o 5
10.2%
d 4
8.2%
h 4
8.2%
H 4
8.2%
a 4
8.2%
n 4
8.2%
s 3
6.1%
B 3
6.1%
Other values (7) 7
14.3%

goalkeeper_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3966
Missing (%)99.4%
Memory size31.3 KiB

goalkeeper_outcome
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)60.0%
Missing3981
Missing (%)99.7%
Memory size31.3 KiB
In Play Danger
3 
No Touch
2 
Success
2 
Touched In
1 
In Play Safe
1 

Length

Max length14
Median length11.5
Mean length10.5
Min length7

Characters and Unicode

Total characters105
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st rowIn Play Danger
2nd rowTouched In
3rd rowNo Touch
4th rowIn Play Danger
5th rowIn Play Danger

Common Values

ValueCountFrequency (%)
In Play Danger 3
 
0.1%
No Touch 2
 
0.1%
Success 2
 
0.1%
Touched In 1
 
< 0.1%
In Play Safe 1
 
< 0.1%
Saved Twice 1
 
< 0.1%
(Missing) 3981
99.7%

Length

2023-05-19T13:56:36.491659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:36.666692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
in 5
22.7%
play 4
18.2%
danger 3
13.6%
no 2
 
9.1%
touch 2
 
9.1%
success 2
 
9.1%
touched 1
 
4.5%
safe 1
 
4.5%
saved 1
 
4.5%
twice 1
 
4.5%

Most occurring characters

ValueCountFrequency (%)
12
 
11.4%
e 9
 
8.6%
a 9
 
8.6%
c 8
 
7.6%
n 8
 
7.6%
I 5
 
4.8%
u 5
 
4.8%
o 5
 
4.8%
y 4
 
3.8%
T 4
 
3.8%
Other values (14) 36
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71
67.6%
Uppercase Letter 22
 
21.0%
Space Separator 12
 
11.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9
12.7%
a 9
12.7%
c 8
11.3%
n 8
11.3%
u 5
7.0%
o 5
7.0%
y 4
 
5.6%
l 4
 
5.6%
s 4
 
5.6%
g 3
 
4.2%
Other values (7) 12
16.9%
Uppercase Letter
ValueCountFrequency (%)
I 5
22.7%
T 4
18.2%
P 4
18.2%
S 4
18.2%
D 3
13.6%
N 2
 
9.1%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93
88.6%
Common 12
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9
 
9.7%
a 9
 
9.7%
c 8
 
8.6%
n 8
 
8.6%
I 5
 
5.4%
u 5
 
5.4%
o 5
 
5.4%
y 4
 
4.3%
T 4
 
4.3%
l 4
 
4.3%
Other values (13) 32
34.4%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
 
11.4%
e 9
 
8.6%
a 9
 
8.6%
c 8
 
7.6%
n 8
 
7.6%
I 5
 
4.8%
u 5
 
4.8%
o 5
 
4.8%
y 4
 
3.8%
T 4
 
3.8%
Other values (14) 36
34.3%

goalkeeper_position
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)9.4%
Missing3959
Missing (%)99.2%
Memory size31.3 KiB
Set
28 
Prone
 
2
Moving
 
2

Length

Max length6
Median length3
Mean length3.3125
Min length3

Characters and Unicode

Total characters106
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSet
2nd rowSet
3rd rowSet
4th rowSet
5th rowSet

Common Values

ValueCountFrequency (%)
Set 28
 
0.7%
Prone 2
 
0.1%
Moving 2
 
0.1%
(Missing) 3959
99.2%

Length

2023-05-19T13:56:36.980832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:37.273486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
set 28
87.5%
prone 2
 
6.2%
moving 2
 
6.2%

Most occurring characters

ValueCountFrequency (%)
e 30
28.3%
S 28
26.4%
t 28
26.4%
o 4
 
3.8%
n 4
 
3.8%
P 2
 
1.9%
r 2
 
1.9%
M 2
 
1.9%
v 2
 
1.9%
i 2
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74
69.8%
Uppercase Letter 32
30.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30
40.5%
t 28
37.8%
o 4
 
5.4%
n 4
 
5.4%
r 2
 
2.7%
v 2
 
2.7%
i 2
 
2.7%
g 2
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
S 28
87.5%
P 2
 
6.2%
M 2
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30
28.3%
S 28
26.4%
t 28
26.4%
o 4
 
3.8%
n 4
 
3.8%
P 2
 
1.9%
r 2
 
1.9%
M 2
 
1.9%
v 2
 
1.9%
i 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30
28.3%
S 28
26.4%
t 28
26.4%
o 4
 
3.8%
n 4
 
3.8%
P 2
 
1.9%
r 2
 
1.9%
M 2
 
1.9%
v 2
 
1.9%
i 2
 
1.9%

goalkeeper_technique
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)28.6%
Missing3984
Missing (%)99.8%
Memory size31.3 KiB
Standing
4 
Diving
3 

Length

Max length8
Median length8
Mean length7.1428571
Min length6

Characters and Unicode

Total characters50
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiving
2nd rowStanding
3rd rowDiving
4th rowStanding
5th rowDiving

Common Values

ValueCountFrequency (%)
Standing 4
 
0.1%
Diving 3
 
0.1%
(Missing) 3984
99.8%

Length

2023-05-19T13:56:37.481198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:37.675654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
standing 4
57.1%
diving 3
42.9%

Most occurring characters

ValueCountFrequency (%)
n 11
22.0%
i 10
20.0%
g 7
14.0%
S 4
 
8.0%
t 4
 
8.0%
a 4
 
8.0%
d 4
 
8.0%
D 3
 
6.0%
v 3
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43
86.0%
Uppercase Letter 7
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 11
25.6%
i 10
23.3%
g 7
16.3%
t 4
 
9.3%
a 4
 
9.3%
d 4
 
9.3%
v 3
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
S 4
57.1%
D 3
42.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 50
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 11
22.0%
i 10
20.0%
g 7
14.0%
S 4
 
8.0%
t 4
 
8.0%
a 4
 
8.0%
d 4
 
8.0%
D 3
 
6.0%
v 3
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 11
22.0%
i 10
20.0%
g 7
14.0%
S 4
 
8.0%
t 4
 
8.0%
a 4
 
8.0%
d 4
 
8.0%
D 3
 
6.0%
v 3
 
6.0%

goalkeeper_type
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)14.3%
Missing3956
Missing (%)99.1%
Memory size31.3 KiB
Shot Faced
25 
Shot Saved
4 
Goal Conceded
3 
Punch
 
2
Collected
 
1

Length

Max length13
Median length10
Mean length9.9428571
Min length5

Characters and Unicode

Total characters348
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st rowPunch
2nd rowShot Faced
3rd rowGoal Conceded
4th rowShot Faced
5th rowShot Faced

Common Values

ValueCountFrequency (%)
Shot Faced 25
 
0.6%
Shot Saved 4
 
0.1%
Goal Conceded 3
 
0.1%
Punch 2
 
0.1%
Collected 1
 
< 0.1%
(Missing) 3956
99.1%

Length

2023-05-19T13:56:37.845321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:38.164476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
shot 29
43.3%
faced 25
37.3%
saved 4
 
6.0%
goal 3
 
4.5%
conceded 3
 
4.5%
punch 2
 
3.0%
collected 1
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 37
10.6%
d 36
10.3%
o 36
10.3%
S 33
9.5%
32
9.2%
a 32
9.2%
c 31
8.9%
h 31
8.9%
t 30
8.6%
F 25
7.2%
Other values (7) 25
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 249
71.6%
Uppercase Letter 67
 
19.3%
Space Separator 32
 
9.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 37
14.9%
d 36
14.5%
o 36
14.5%
a 32
12.9%
c 31
12.4%
h 31
12.4%
t 30
12.0%
l 5
 
2.0%
n 5
 
2.0%
v 4
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
S 33
49.3%
F 25
37.3%
C 4
 
6.0%
G 3
 
4.5%
P 2
 
3.0%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316
90.8%
Common 32
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 37
11.7%
d 36
11.4%
o 36
11.4%
S 33
10.4%
a 32
10.1%
c 31
9.8%
h 31
9.8%
t 30
9.5%
F 25
7.9%
l 5
 
1.6%
Other values (6) 20
6.3%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 37
10.6%
d 36
10.3%
o 36
10.3%
S 33
9.5%
32
9.2%
a 32
9.2%
c 31
8.9%
h 31
8.9%
t 30
8.6%
F 25
7.2%
Other values (7) 25
7.2%

id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct3991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
0af194ba-5206-40b2-a5e9-3353177eefc3
 
1
06400065-8e7a-4258-aec9-d6d0bdfbe394
 
1
284661ef-2178-4034-a66f-f02e65a0848c
 
1
4732de54-c916-432b-9a22-486f8fa76bde
 
1
c82ef6ba-6eed-4560-8e82-e347c76df717
 
1
Other values (3986)
3986 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters143676
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3991 ?
Unique (%)100.0%

Sample

1st row0af194ba-5206-40b2-a5e9-3353177eefc3
2nd row2d42b07b-a2dd-449d-932e-665ca5155f24
3rd row37839e16-1d3d-41cc-9ed9-315216e58680
4th rowda925325-bf15-49e2-a891-dcb662c01904
5th row22c32b57-12fe-402f-aa92-71f0110e7ddd

Common Values

ValueCountFrequency (%)
0af194ba-5206-40b2-a5e9-3353177eefc3 1
 
< 0.1%
06400065-8e7a-4258-aec9-d6d0bdfbe394 1
 
< 0.1%
284661ef-2178-4034-a66f-f02e65a0848c 1
 
< 0.1%
4732de54-c916-432b-9a22-486f8fa76bde 1
 
< 0.1%
c82ef6ba-6eed-4560-8e82-e347c76df717 1
 
< 0.1%
932fed56-3d31-48d1-9b18-7be47df90e4f 1
 
< 0.1%
6fa1ccb3-eb3e-4bb9-8360-4a1f2ee184bc 1
 
< 0.1%
a59b5d86-7b48-4a0c-b275-3cb35dcd9aec 1
 
< 0.1%
00a928f5-2116-433e-a989-fa29c7954ade 1
 
< 0.1%
753ab915-ef2d-43cb-a2d2-41b3fdf886f5 1
 
< 0.1%
Other values (3981) 3981
99.7%

Length

2023-05-19T13:56:38.343363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0af194ba-5206-40b2-a5e9-3353177eefc3 1
 
< 0.1%
be4a6df8-bec2-4a89-a75b-a11dc6d6bc0d 1
 
< 0.1%
7ced7ebb-2708-455d-8dba-28d8ee605942 1
 
< 0.1%
37839e16-1d3d-41cc-9ed9-315216e58680 1
 
< 0.1%
da925325-bf15-49e2-a891-dcb662c01904 1
 
< 0.1%
22c32b57-12fe-402f-aa92-71f0110e7ddd 1
 
< 0.1%
c33f8366-31b0-4bb1-b891-c3e6a9974a2f 1
 
< 0.1%
4bb5ef98-f35a-46a0-86d2-4300f39bdce7 1
 
< 0.1%
5f18fc92-4412-47db-80fe-8bb3d78da122 1
 
< 0.1%
f92ab228-1310-448b-83d0-86a44ff555a1 1
 
< 0.1%
Other values (3981) 3981
99.7%

Most occurring characters

ValueCountFrequency (%)
- 15964
 
11.1%
4 11489
 
8.0%
8 8699
 
6.1%
9 8444
 
5.9%
b 8413
 
5.9%
a 8351
 
5.8%
2 7651
 
5.3%
d 7543
 
5.3%
3 7516
 
5.2%
1 7515
 
5.2%
Other values (7) 52091
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81196
56.5%
Lowercase Letter 46516
32.4%
Dash Punctuation 15964
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 11489
14.1%
8 8699
10.7%
9 8444
10.4%
2 7651
9.4%
3 7516
9.3%
1 7515
9.3%
7 7510
9.2%
5 7488
9.2%
0 7476
9.2%
6 7408
9.1%
Lowercase Letter
ValueCountFrequency (%)
b 8413
18.1%
a 8351
18.0%
d 7543
16.2%
f 7464
16.0%
c 7389
15.9%
e 7356
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 15964
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97160
67.6%
Latin 46516
32.4%

Most frequent character per script

Common
ValueCountFrequency (%)
- 15964
16.4%
4 11489
11.8%
8 8699
9.0%
9 8444
8.7%
2 7651
7.9%
3 7516
7.7%
1 7515
7.7%
7 7510
7.7%
5 7488
7.7%
0 7476
7.7%
Latin
ValueCountFrequency (%)
b 8413
18.1%
a 8351
18.0%
d 7543
16.2%
f 7464
16.0%
c 7389
15.9%
e 7356
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 15964
 
11.1%
4 11489
 
8.0%
8 8699
 
6.1%
9 8444
 
5.9%
b 8413
 
5.9%
a 8351
 
5.8%
2 7651
 
5.3%
d 7543
 
5.3%
3 7516
 
5.2%
1 7515
 
5.2%
Other values (7) 52091
36.3%

df_index
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct3991
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996
Minimum1
Maximum3991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:38.607345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile200.5
Q1998.5
median1996
Q32993.5
95-th percentile3791.5
Maximum3991
Range3990
Interquartile range (IQR)1995

Descriptive statistics

Standard deviation1152.2468
Coefficient of variation (CV)0.57727795
Kurtosis-1.2
Mean1996
Median Absolute Deviation (MAD)998
Skewness0
Sum7966036
Variance1327672.7
MonotonicityNot monotonic
2023-05-19T13:56:38.885920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2103 1
 
< 0.1%
2061 1
 
< 0.1%
2064 1
 
< 0.1%
2067 1
 
< 0.1%
2070 1
 
< 0.1%
2073 1
 
< 0.1%
2076 1
 
< 0.1%
2079 1
 
< 0.1%
2082 1
 
< 0.1%
Other values (3981) 3981
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3991 1
< 0.1%
3990 1
< 0.1%
3989 1
< 0.1%
3988 1
< 0.1%
3987 1
< 0.1%
3986 1
< 0.1%
3985 1
< 0.1%
3984 1
< 0.1%
3983 1
< 0.1%
3982 1
< 0.1%

interception_outcome
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)15.0%
Missing3971
Missing (%)99.5%
Memory size31.3 KiB
Won
8 
Success In Play
6 
Lost In Play
6 

Length

Max length15
Median length12
Mean length9.3
Min length3

Characters and Unicode

Total characters186
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuccess In Play
2nd rowWon
3rd rowWon
4th rowWon
5th rowSuccess In Play

Common Values

ValueCountFrequency (%)
Won 8
 
0.2%
Success In Play 6
 
0.2%
Lost In Play 6
 
0.2%
(Missing) 3971
99.5%

Length

2023-05-19T13:56:39.161077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:39.438397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
in 12
27.3%
play 12
27.3%
won 8
18.2%
success 6
13.6%
lost 6
13.6%

Most occurring characters

ValueCountFrequency (%)
24
12.9%
n 20
10.8%
s 18
9.7%
o 14
 
7.5%
c 12
 
6.5%
I 12
 
6.5%
P 12
 
6.5%
l 12
 
6.5%
a 12
 
6.5%
y 12
 
6.5%
Other values (6) 38
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 118
63.4%
Uppercase Letter 44
 
23.7%
Space Separator 24
 
12.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 20
16.9%
s 18
15.3%
o 14
11.9%
c 12
10.2%
l 12
10.2%
a 12
10.2%
y 12
10.2%
u 6
 
5.1%
e 6
 
5.1%
t 6
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
I 12
27.3%
P 12
27.3%
W 8
18.2%
S 6
13.6%
L 6
13.6%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162
87.1%
Common 24
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 20
12.3%
s 18
11.1%
o 14
8.6%
c 12
 
7.4%
I 12
 
7.4%
P 12
 
7.4%
l 12
 
7.4%
a 12
 
7.4%
y 12
 
7.4%
W 8
 
4.9%
Other values (5) 30
18.5%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24
12.9%
n 20
10.8%
s 18
9.7%
o 14
 
7.5%
c 12
 
6.5%
I 12
 
6.5%
P 12
 
6.5%
l 12
 
6.5%
a 12
 
6.5%
y 12
 
6.5%
Other values (6) 38
20.4%

location
Unsupported

REJECTED  UNSUPPORTED 

Missing25
Missing (%)0.6%
Memory size31.3 KiB

match_id
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
3773497
3991 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters27937
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3773497
2nd row3773497
3rd row3773497
4th row3773497
5th row3773497

Common Values

ValueCountFrequency (%)
3773497 3991
100.0%

Length

2023-05-19T13:56:39.601578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:39.765218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3773497 3991
100.0%

Most occurring characters

ValueCountFrequency (%)
7 11973
42.9%
3 7982
28.6%
4 3991
 
14.3%
9 3991
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27937
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 11973
42.9%
3 7982
28.6%
4 3991
 
14.3%
9 3991
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 27937
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 11973
42.9%
3 7982
28.6%
4 3991
 
14.3%
9 3991
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 11973
42.9%
3 7982
28.6%
4 3991
 
14.3%
9 3991
 
14.3%

minute
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.294412
Minimum0
Maximum94
Zeros94
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:39.965039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median43
Q360
95-th percentile87
Maximum94
Range94
Interquartile range (IQR)43

Descriptive statistics

Standard deviation26.280409
Coefficient of variation (CV)0.63641563
Kurtosis-0.98606602
Mean41.294412
Median Absolute Deviation (MAD)21
Skewness0.18398424
Sum164806
Variance690.65992
MonotonicityNot monotonic
2023-05-19T13:56:40.282897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 125
 
3.1%
45 113
 
2.8%
0 94
 
2.4%
12 92
 
2.3%
15 90
 
2.3%
20 86
 
2.2%
32 86
 
2.2%
29 78
 
2.0%
2 76
 
1.9%
43 76
 
1.9%
Other values (81) 3075
77.0%
ValueCountFrequency (%)
0 94
2.4%
1 63
1.6%
2 76
1.9%
3 69
1.7%
4 40
1.0%
5 65
1.6%
6 47
1.2%
7 27
 
0.7%
8 72
1.8%
9 32
 
0.8%
ValueCountFrequency (%)
94 3
 
0.1%
93 35
0.9%
92 40
1.0%
91 23
0.6%
90 4
 
0.1%
89 29
0.7%
88 38
1.0%
87 30
0.8%
86 41
1.0%
85 51
1.3%

miscontrol_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3990
Missing (%)> 99.9%
Memory size31.3 KiB
True
 
1
(Missing)
3990 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 3990
> 99.9%
2023-05-19T13:56:40.637096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

off_camera
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)2.1%
Missing3944
Missing (%)98.8%
Memory size31.3 KiB
True
 
47
(Missing)
3944 
ValueCountFrequency (%)
True 47
 
1.2%
(Missing) 3944
98.8%
2023-05-19T13:56:40.818197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

out
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)2.9%
Missing3956
Missing (%)99.1%
Memory size31.3 KiB
True
 
35
(Missing)
3956 
ValueCountFrequency (%)
True 35
 
0.9%
(Missing) 3956
99.1%
2023-05-19T13:56:41.225790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)8.3%
Missing3979
Missing (%)99.7%
Memory size31.3 KiB
True
 
12
(Missing)
3979 
ValueCountFrequency (%)
True 12
 
0.3%
(Missing) 3979
99.7%
2023-05-19T13:56:41.456743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_angle
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1077
Distinct (%)97.6%
Missing2888
Missing (%)72.4%
Infinite0
Infinite (%)0.0%
Mean-0.0036237796
Minimum-3.0968463
Maximum3.1415927
Zeros3
Zeros (%)0.1%
Negative554
Negative (%)13.9%
Memory size31.3 KiB
2023-05-19T13:56:41.789462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.0968463
5-th percentile-2.5922816
Q1-1.3221842
median-0.02677931
Q31.2775808
95-th percentile2.6799277
Maximum3.1415927
Range6.238439
Interquartile range (IQR)2.599765

Descriptive statistics

Standard deviation1.5971365
Coefficient of variation (CV)-440.73776
Kurtosis-0.93960942
Mean-0.0036237796
Median Absolute Deviation (MAD)1.2994195
Skewness0.041749199
Sum-3.9970289
Variance2.550845
MonotonicityNot monotonic
2023-05-19T13:56:42.339238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5707964 6
 
0.2%
-1.5707964 4
 
0.1%
0 3
 
0.1%
-1.1441689 2
 
0.1%
-0.7981634 2
 
0.1%
-1.4801364 2
 
0.1%
0.40363392 2
 
0.1%
-1.8925469 2
 
0.1%
1.0487047 2
 
0.1%
-0.2376123 2
 
0.1%
Other values (1067) 1076
 
27.0%
(Missing) 2888
72.4%
ValueCountFrequency (%)
-3.0968463 1
< 0.1%
-3.0934372 1
< 0.1%
-3.0883098 1
< 0.1%
-3.0683296 1
< 0.1%
-3.0671039 1
< 0.1%
-3.0584514 1
< 0.1%
-3.0542245 1
< 0.1%
-3.041924 1
< 0.1%
-3.0360262 1
< 0.1%
-3.0292852 1
< 0.1%
ValueCountFrequency (%)
3.1415927 2
0.1%
3.1174037 1
< 0.1%
3.1093457 1
< 0.1%
3.1042268 1
< 0.1%
3.090542 1
< 0.1%
3.0890095 1
< 0.1%
3.0850492 1
< 0.1%
3.0799425 1
< 0.1%
3.071668 1
< 0.1%
3.0668423 1
< 0.1%

pass_assisted_shot_id
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct20
Distinct (%)100.0%
Missing3971
Missing (%)99.5%
Memory size31.3 KiB
aa4ca83a-c0aa-42e4-ab1a-e111f9bd08bf
 
1
cefa3ae6-3e88-4cd3-a5f7-11d97849c34a
 
1
63749ee6-e75f-47de-9456-409863b6de25
 
1
f735984a-b457-40e4-b619-7effb3a0fa05
 
1
9e93d626-8eae-4ebd-bc4a-9d5ae238ccd2
 
1
Other values (15)
15 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters720
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st rowe6c2cd02-5cce-47a8-8711-387c01899056
2nd row6550f445-4886-4645-9441-c852263c8f76
3rd rowe3d86f03-1c76-4e07-9628-019a90c7855d
4th rowd3b69446-7f2c-4317-94d4-ec5e75f53d5c
5th rowe3ba43bf-cbeb-43cd-8bc7-bec2d661ae16

Common Values

ValueCountFrequency (%)
aa4ca83a-c0aa-42e4-ab1a-e111f9bd08bf 1
 
< 0.1%
cefa3ae6-3e88-4cd3-a5f7-11d97849c34a 1
 
< 0.1%
63749ee6-e75f-47de-9456-409863b6de25 1
 
< 0.1%
f735984a-b457-40e4-b619-7effb3a0fa05 1
 
< 0.1%
9e93d626-8eae-4ebd-bc4a-9d5ae238ccd2 1
 
< 0.1%
3123a541-4531-4d72-9299-91d08d1005e4 1
 
< 0.1%
2f5e7cf3-f5f7-4625-ad61-382c4c9e88fe 1
 
< 0.1%
9c5abeff-0f11-4e58-ab92-8bd6d8f87a07 1
 
< 0.1%
a5174b21-5628-4c86-b0eb-d2893b627195 1
 
< 0.1%
c0da78a2-902f-4f71-8ec4-7c00105c159c 1
 
< 0.1%
Other values (10) 10
 
0.3%
(Missing) 3971
99.5%

Length

2023-05-19T13:56:42.708337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aa4ca83a-c0aa-42e4-ab1a-e111f9bd08bf 1
 
5.0%
cefa3ae6-3e88-4cd3-a5f7-11d97849c34a 1
 
5.0%
6550f445-4886-4645-9441-c852263c8f76 1
 
5.0%
e3d86f03-1c76-4e07-9628-019a90c7855d 1
 
5.0%
d3b69446-7f2c-4317-94d4-ec5e75f53d5c 1
 
5.0%
e3ba43bf-cbeb-43cd-8bc7-bec2d661ae16 1
 
5.0%
65c21002-6430-4202-93ca-71964ebd2c8c 1
 
5.0%
ee438bca-2b8f-479a-be8b-453890729d30 1
 
5.0%
aaa1fc98-e20c-4694-933c-1d9f8de21766 1
 
5.0%
eac014e6-1d2b-4c84-9473-c56106932718 1
 
5.0%
Other values (10) 10
50.0%

Most occurring characters

ValueCountFrequency (%)
- 80
 
11.1%
4 56
 
7.8%
c 46
 
6.4%
8 46
 
6.4%
1 46
 
6.4%
9 45
 
6.2%
e 44
 
6.1%
6 41
 
5.7%
a 40
 
5.6%
2 39
 
5.4%
Other values (7) 237
32.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
58.1%
Lowercase Letter 222
30.8%
Dash Punctuation 80
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 56
13.4%
8 46
11.0%
1 46
11.0%
9 45
10.8%
6 41
9.8%
2 39
9.3%
5 38
9.1%
0 36
8.6%
3 36
8.6%
7 35
8.4%
Lowercase Letter
ValueCountFrequency (%)
c 46
20.7%
e 44
19.8%
a 40
18.0%
d 33
14.9%
b 30
13.5%
f 29
13.1%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 498
69.2%
Latin 222
30.8%

Most frequent character per script

Common
ValueCountFrequency (%)
- 80
16.1%
4 56
11.2%
8 46
9.2%
1 46
9.2%
9 45
9.0%
6 41
8.2%
2 39
7.8%
5 38
7.6%
0 36
7.2%
3 36
7.2%
Latin
ValueCountFrequency (%)
c 46
20.7%
e 44
19.8%
a 40
18.0%
d 33
14.9%
b 30
13.5%
f 29
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 80
 
11.1%
4 56
 
7.8%
c 46
 
6.4%
8 46
 
6.4%
1 46
 
6.4%
9 45
 
6.2%
e 44
 
6.1%
6 41
 
5.7%
a 40
 
5.6%
2 39
 
5.4%
Other values (7) 237
32.9%

pass_body_part
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct7
Distinct (%)0.7%
Missing2941
Missing (%)73.7%
Memory size31.3 KiB
Right Foot
655 
Left Foot
365 
Head
 
12
Keeper Arm
 
7
No Touch
 
4
Other values (2)
 
7

Length

Max length10
Median length10
Mean length9.5580952
Min length4

Characters and Unicode

Total characters10036
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeft Foot
2nd rowRight Foot
3rd rowLeft Foot
4th rowRight Foot
5th rowLeft Foot

Common Values

ValueCountFrequency (%)
Right Foot 655
 
16.4%
Left Foot 365
 
9.1%
Head 12
 
0.3%
Keeper Arm 7
 
0.2%
No Touch 4
 
0.1%
Drop Kick 4
 
0.1%
Other 3
 
0.1%
(Missing) 2941
73.7%

Length

2023-05-19T13:56:43.062763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:43.377418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
foot 1020
48.9%
right 655
31.4%
left 365
 
17.5%
head 12
 
0.6%
keeper 7
 
0.3%
arm 7
 
0.3%
no 4
 
0.2%
touch 4
 
0.2%
drop 4
 
0.2%
kick 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 2052
20.4%
t 2043
20.4%
1035
10.3%
F 1020
10.2%
h 662
 
6.6%
i 659
 
6.6%
R 655
 
6.5%
g 655
 
6.5%
e 401
 
4.0%
L 365
 
3.6%
Other values (16) 489
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6916
68.9%
Uppercase Letter 2085
 
20.8%
Space Separator 1035
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2052
29.7%
t 2043
29.5%
h 662
 
9.6%
i 659
 
9.5%
g 655
 
9.5%
e 401
 
5.8%
f 365
 
5.3%
r 21
 
0.3%
d 12
 
0.2%
a 12
 
0.2%
Other values (5) 34
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
F 1020
48.9%
R 655
31.4%
L 365
 
17.5%
H 12
 
0.6%
K 11
 
0.5%
A 7
 
0.3%
N 4
 
0.2%
T 4
 
0.2%
D 4
 
0.2%
O 3
 
0.1%
Space Separator
ValueCountFrequency (%)
1035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9001
89.7%
Common 1035
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 2052
22.8%
t 2043
22.7%
F 1020
11.3%
h 662
 
7.4%
i 659
 
7.3%
R 655
 
7.3%
g 655
 
7.3%
e 401
 
4.5%
L 365
 
4.1%
f 365
 
4.1%
Other values (15) 124
 
1.4%
Common
ValueCountFrequency (%)
1035
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 2052
20.4%
t 2043
20.4%
1035
10.3%
F 1020
10.2%
h 662
 
6.6%
i 659
 
6.6%
R 655
 
6.5%
g 655
 
6.5%
e 401
 
4.0%
L 365
 
3.6%
Other values (16) 489
 
4.9%

pass_cross
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)4.5%
Missing3969
Missing (%)99.4%
Memory size31.3 KiB
True
 
22
(Missing)
3969 
ValueCountFrequency (%)
True 22
 
0.6%
(Missing) 3969
99.4%
2023-05-19T13:56:43.695368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_deflected
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3990
Missing (%)> 99.9%
Memory size31.3 KiB
True
 
1
(Missing)
3990 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 3990
> 99.9%
2023-05-19T13:56:43.929494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2888
Missing (%)72.4%
Memory size31.3 KiB

pass_goal_assist
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:44.134498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_height
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.3%
Missing2888
Missing (%)72.4%
Memory size31.3 KiB
Ground Pass
847 
High Pass
131 
Low Pass
125 

Length

Max length11
Median length11
Mean length10.422484
Min length8

Characters and Unicode

Total characters11496
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGround Pass
2nd rowGround Pass
3rd rowGround Pass
4th rowGround Pass
5th rowGround Pass

Common Values

ValueCountFrequency (%)
Ground Pass 847
 
21.2%
High Pass 131
 
3.3%
Low Pass 125
 
3.1%
(Missing) 2888
72.4%

Length

2023-05-19T13:56:44.806802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:45.330090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
pass 1103
50.0%
ground 847
38.4%
high 131
 
5.9%
low 125
 
5.7%

Most occurring characters

ValueCountFrequency (%)
s 2206
19.2%
1103
9.6%
P 1103
9.6%
a 1103
9.6%
o 972
8.5%
G 847
 
7.4%
r 847
 
7.4%
u 847
 
7.4%
n 847
 
7.4%
d 847
 
7.4%
Other values (6) 774
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8187
71.2%
Uppercase Letter 2206
 
19.2%
Space Separator 1103
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2206
26.9%
a 1103
13.5%
o 972
11.9%
r 847
 
10.3%
u 847
 
10.3%
n 847
 
10.3%
d 847
 
10.3%
i 131
 
1.6%
g 131
 
1.6%
h 131
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
P 1103
50.0%
G 847
38.4%
H 131
 
5.9%
L 125
 
5.7%
Space Separator
ValueCountFrequency (%)
1103
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10393
90.4%
Common 1103
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2206
21.2%
P 1103
10.6%
a 1103
10.6%
o 972
9.4%
G 847
 
8.1%
r 847
 
8.1%
u 847
 
8.1%
n 847
 
8.1%
d 847
 
8.1%
H 131
 
1.3%
Other values (5) 643
 
6.2%
Common
ValueCountFrequency (%)
1103
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 2206
19.2%
1103
9.6%
P 1103
9.6%
a 1103
9.6%
o 972
8.5%
G 847
 
7.4%
r 847
 
7.4%
u 847
 
7.4%
n 847
 
7.4%
d 847
 
7.4%
Other values (6) 774
 
6.7%

pass_inswinging
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:45.649627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_length
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1062
Distinct (%)96.3%
Missing2888
Missing (%)72.4%
Infinite0
Infinite (%)0.0%
Mean18.349877
Minimum1.4866068
Maximum111.50542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:45.937923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.4866068
5-th percentile5.3811808
Q19.884078
median15.121178
Q322.508555
95-th percentile43.301639
Maximum111.50542
Range110.01882
Interquartile range (IQR)12.624477

Descriptive statistics

Standard deviation13.078002
Coefficient of variation (CV)0.71270242
Kurtosis9.1077389
Mean18.349877
Median Absolute Deviation (MAD)5.771404
Skewness2.3996219
Sum20239.915
Variance171.03414
MonotonicityNot monotonic
2023-05-19T13:56:46.288910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.403628 3
 
0.1%
7.1589108 3
 
0.1%
10.917875 2
 
0.1%
21.661486 2
 
0.1%
13.813399 2
 
0.1%
3.522783 2
 
0.1%
4.669047 2
 
0.1%
9.035485 2
 
0.1%
21.44621 2
 
0.1%
19.358202 2
 
0.1%
Other values (1052) 1081
 
27.1%
(Missing) 2888
72.4%
ValueCountFrequency (%)
1.4866068 1
< 0.1%
1.9104973 1
< 0.1%
2.1540658 1
< 0.1%
2.6172504 1
< 0.1%
2.7856777 1
< 0.1%
2.9274561 1
< 0.1%
2.9832869 1
< 0.1%
3.0463092 1
< 0.1%
3.138471 1
< 0.1%
3.2 1
< 0.1%
ValueCountFrequency (%)
111.505424 1
< 0.1%
108.82909 1
< 0.1%
103.87878 1
< 0.1%
91.49038 1
< 0.1%
79.83107 1
< 0.1%
76.45469 1
< 0.1%
74.696724 1
< 0.1%
73.08037 1
< 0.1%
72.15435 1
< 0.1%
69.6995 1
< 0.1%

pass_miscommunication
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:46.720340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_no_touch
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing3987
Missing (%)99.9%
Memory size31.3 KiB
True
 
4
(Missing)
3987 
ValueCountFrequency (%)
True 4
 
0.1%
(Missing) 3987
99.9%
2023-05-19T13:56:46.975768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_outcome
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)3.0%
Missing3823
Missing (%)95.8%
Memory size31.3 KiB
Incomplete
141 
Out
20 
Unknown
 
4
Injury Clearance
 
2
Pass Offside
 
1

Length

Max length16
Median length10
Mean length9.1785714
Min length3

Characters and Unicode

Total characters1542
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowIncomplete
2nd rowIncomplete
3rd rowIncomplete
4th rowIncomplete
5th rowIncomplete

Common Values

ValueCountFrequency (%)
Incomplete 141
 
3.5%
Out 20
 
0.5%
Unknown 4
 
0.1%
Injury Clearance 2
 
0.1%
Pass Offside 1
 
< 0.1%
(Missing) 3823
95.8%

Length

2023-05-19T13:56:47.211773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:47.521932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
incomplete 141
82.5%
out 20
 
11.7%
unknown 4
 
2.3%
injury 2
 
1.2%
clearance 2
 
1.2%
pass 1
 
0.6%
offside 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 287
18.6%
t 161
10.4%
n 157
10.2%
o 145
9.4%
I 143
9.3%
c 143
9.3%
l 143
9.3%
m 141
9.1%
p 141
9.1%
u 22
 
1.4%
Other values (15) 59
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1368
88.7%
Uppercase Letter 171
 
11.1%
Space Separator 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 287
21.0%
t 161
11.8%
n 157
11.5%
o 145
10.6%
c 143
10.5%
l 143
10.5%
m 141
10.3%
p 141
10.3%
u 22
 
1.6%
a 5
 
0.4%
Other values (9) 23
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
I 143
83.6%
O 21
 
12.3%
U 4
 
2.3%
C 2
 
1.2%
P 1
 
0.6%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1539
99.8%
Common 3
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 287
18.6%
t 161
10.5%
n 157
10.2%
o 145
9.4%
I 143
9.3%
c 143
9.3%
l 143
9.3%
m 141
9.2%
p 141
9.2%
u 22
 
1.4%
Other values (14) 56
 
3.6%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 287
18.6%
t 161
10.4%
n 157
10.2%
o 145
9.4%
I 143
9.3%
c 143
9.3%
l 143
9.3%
m 141
9.1%
p 141
9.1%
u 22
 
1.4%
Other values (15) 59
 
3.8%

pass_outswinging
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)50.0%
Missing3989
Missing (%)99.9%
Memory size31.3 KiB
True
 
2
(Missing)
3989 
ValueCountFrequency (%)
True 2
 
0.1%
(Missing) 3989
99.9%
2023-05-19T13:56:47.811293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_recipient
Categorical

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)3.1%
Missing2948
Missing (%)73.9%
Memory size31.3 KiB
Lionel Andrés Messi Cuccittini
90 
Clément Lenglet
87 
Jordi Alba Ramos
74 
Frenkie de Jong
73 
Óscar Mingueza García
73 
Other values (27)
646 

Length

Max length48
Median length30
Mean length20.798658
Min length10

Characters and Unicode

Total characters21693
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSergio Busquets i Burgos
2nd rowClément Lenglet
3rd rowSergio Busquets i Burgos
4th rowClément Lenglet
5th rowJordi Alba Ramos

Common Values

ValueCountFrequency (%)
Lionel Andrés Messi Cuccittini 90
 
2.3%
Clément Lenglet 87
 
2.2%
Jordi Alba Ramos 74
 
1.9%
Frenkie de Jong 73
 
1.8%
Óscar Mingueza García 73
 
1.8%
Sergio Busquets i Burgos 70
 
1.8%
Pedro González López 69
 
1.7%
Luka Modrić 40
 
1.0%
Ronald Federico Araújo da Silva 38
 
1.0%
Vinícius José Paixão de Oliveira Júnior 36
 
0.9%
Other values (22) 393
 
9.8%
(Missing) 2948
73.9%

Length

2023-05-19T13:56:48.063063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 113
 
3.5%
cuccittini 90
 
2.8%
lionel 90
 
2.8%
messi 90
 
2.8%
andrés 90
 
2.8%
lenglet 87
 
2.7%
clément 87
 
2.7%
jordi 74
 
2.3%
alba 74
 
2.3%
ramos 74
 
2.3%
Other values (83) 2363
73.1%

Most occurring characters

ValueCountFrequency (%)
2189
 
10.1%
e 2006
 
9.2%
i 1722
 
7.9%
r 1409
 
6.5%
o 1390
 
6.4%
a 1353
 
6.2%
n 1349
 
6.2%
s 1011
 
4.7%
l 775
 
3.6%
t 687
 
3.2%
Other values (46) 7802
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16480
76.0%
Uppercase Letter 3007
 
13.9%
Space Separator 2189
 
10.1%
Dash Punctuation 17
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2006
12.2%
i 1722
10.4%
r 1409
 
8.5%
o 1390
 
8.4%
a 1353
 
8.2%
n 1349
 
8.2%
s 1011
 
6.1%
l 775
 
4.7%
t 687
 
4.2%
d 661
 
4.0%
Other values (22) 4117
25.0%
Uppercase Letter
ValueCountFrequency (%)
M 316
10.5%
L 307
10.2%
A 280
9.3%
C 275
 
9.1%
J 233
 
7.7%
S 213
 
7.1%
G 187
 
6.2%
F 182
 
6.1%
B 179
 
6.0%
R 141
 
4.7%
Other values (12) 694
23.1%
Space Separator
ValueCountFrequency (%)
2189
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19487
89.8%
Common 2206
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2006
 
10.3%
i 1722
 
8.8%
r 1409
 
7.2%
o 1390
 
7.1%
a 1353
 
6.9%
n 1349
 
6.9%
s 1011
 
5.2%
l 775
 
4.0%
t 687
 
3.5%
d 661
 
3.4%
Other values (44) 7124
36.6%
Common
ValueCountFrequency (%)
2189
99.2%
- 17
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20780
95.8%
None 913
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2189
 
10.5%
e 2006
 
9.7%
i 1722
 
8.3%
r 1409
 
6.8%
o 1390
 
6.7%
a 1353
 
6.5%
n 1349
 
6.5%
s 1011
 
4.9%
l 775
 
3.7%
t 687
 
3.3%
Other values (36) 6889
33.2%
None
ValueCountFrequency (%)
é 310
34.0%
í 131
14.3%
á 116
 
12.7%
ó 81
 
8.9%
ú 78
 
8.5%
Ó 73
 
8.0%
ã 56
 
6.1%
ć 40
 
4.4%
É 16
 
1.8%
Á 12
 
1.3%

pass_shot_assist
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)5.6%
Missing3973
Missing (%)99.5%
Memory size31.3 KiB
True
 
18
(Missing)
3973 
ValueCountFrequency (%)
True 18
 
0.5%
(Missing) 3973
99.5%
2023-05-19T13:56:48.367594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_straight
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)25.0%
Missing3987
Missing (%)99.9%
Memory size31.3 KiB
True
 
4
(Missing)
3987 
ValueCountFrequency (%)
True 4
 
0.1%
(Missing) 3987
99.9%
2023-05-19T13:56:48.597595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_switch
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)5.3%
Missing3972
Missing (%)99.5%
Memory size31.3 KiB
True
 
19
(Missing)
3972 
ValueCountFrequency (%)
True 19
 
0.5%
(Missing) 3972
99.5%
2023-05-19T13:56:48.817946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_technique
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)20.0%
Missing3971
Missing (%)99.5%
Memory size31.3 KiB
Through Ball
12 
Straight
4 
Outswinging
2 
Inswinging
2 

Length

Max length12
Median length12
Mean length10.9
Min length8

Characters and Unicode

Total characters218
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThrough Ball
2nd rowThrough Ball
3rd rowThrough Ball
4th rowOutswinging
5th rowThrough Ball

Common Values

ValueCountFrequency (%)
Through Ball 12
 
0.3%
Straight 4
 
0.1%
Outswinging 2
 
0.1%
Inswinging 2
 
0.1%
(Missing) 3971
99.5%

Length

2023-05-19T13:56:49.058563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:49.396088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
through 12
37.5%
ball 12
37.5%
straight 4
 
12.5%
outswinging 2
 
6.2%
inswinging 2
 
6.2%

Most occurring characters

ValueCountFrequency (%)
h 28
12.8%
l 24
11.0%
g 24
11.0%
a 16
 
7.3%
r 16
 
7.3%
u 14
 
6.4%
i 12
 
5.5%
T 12
 
5.5%
B 12
 
5.5%
12
 
5.5%
Other values (8) 48
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 174
79.8%
Uppercase Letter 32
 
14.7%
Space Separator 12
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 28
16.1%
l 24
13.8%
g 24
13.8%
a 16
9.2%
r 16
9.2%
u 14
8.0%
i 12
6.9%
o 12
6.9%
t 10
 
5.7%
n 10
 
5.7%
Other values (2) 8
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
T 12
37.5%
B 12
37.5%
S 4
 
12.5%
O 2
 
6.2%
I 2
 
6.2%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206
94.5%
Common 12
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
h 28
13.6%
l 24
11.7%
g 24
11.7%
a 16
7.8%
r 16
7.8%
u 14
 
6.8%
i 12
 
5.8%
T 12
 
5.8%
B 12
 
5.8%
o 12
 
5.8%
Other values (7) 36
17.5%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
h 28
12.8%
l 24
11.0%
g 24
11.0%
a 16
 
7.3%
r 16
 
7.3%
u 14
 
6.4%
i 12
 
5.5%
T 12
 
5.5%
B 12
 
5.5%
12
 
5.5%
Other values (8) 48
22.0%

pass_through_ball
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)8.3%
Missing3979
Missing (%)99.7%
Memory size31.3 KiB
True
 
12
(Missing)
3979 
ValueCountFrequency (%)
True 12
 
0.3%
(Missing) 3979
99.7%
2023-05-19T13:56:49.705307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

pass_type
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)4.2%
Missing3824
Missing (%)95.8%
Memory size31.3 KiB
Recovery
62 
Throw-in
41 
Free Kick
23 
Goal Kick
13 
Interception
12 
Other values (2)
16 

Length

Max length12
Median length8
Mean length8.3712575
Min length6

Characters and Unicode

Total characters1398
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKick Off
2nd rowRecovery
3rd rowRecovery
4th rowRecovery
5th rowInterception

Common Values

ValueCountFrequency (%)
Recovery 62
 
1.6%
Throw-in 41
 
1.0%
Free Kick 23
 
0.6%
Goal Kick 13
 
0.3%
Interception 12
 
0.3%
Corner 11
 
0.3%
Kick Off 5
 
0.1%
(Missing) 3824
95.8%

Length

2023-05-19T13:56:49.972929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:50.312596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
recovery 62
29.8%
throw-in 41
19.7%
kick 41
19.7%
free 23
 
11.1%
goal 13
 
6.2%
interception 12
 
5.8%
corner 11
 
5.3%
off 5
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e 205
14.7%
r 160
11.4%
o 139
 
9.9%
c 115
 
8.2%
i 94
 
6.7%
n 76
 
5.4%
R 62
 
4.4%
v 62
 
4.4%
y 62
 
4.4%
K 41
 
2.9%
Other values (16) 382
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1108
79.3%
Uppercase Letter 208
 
14.9%
Space Separator 41
 
2.9%
Dash Punctuation 41
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 205
18.5%
r 160
14.4%
o 139
12.5%
c 115
10.4%
i 94
8.5%
n 76
 
6.9%
v 62
 
5.6%
y 62
 
5.6%
h 41
 
3.7%
w 41
 
3.7%
Other values (6) 113
10.2%
Uppercase Letter
ValueCountFrequency (%)
R 62
29.8%
K 41
19.7%
T 41
19.7%
F 23
 
11.1%
G 13
 
6.2%
I 12
 
5.8%
C 11
 
5.3%
O 5
 
2.4%
Space Separator
ValueCountFrequency (%)
41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1316
94.1%
Common 82
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 205
15.6%
r 160
12.2%
o 139
10.6%
c 115
 
8.7%
i 94
 
7.1%
n 76
 
5.8%
R 62
 
4.7%
v 62
 
4.7%
y 62
 
4.7%
K 41
 
3.1%
Other values (14) 300
22.8%
Common
ValueCountFrequency (%)
41
50.0%
- 41
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 205
14.7%
r 160
11.4%
o 139
 
9.9%
c 115
 
8.2%
i 94
 
6.7%
n 76
 
5.4%
R 62
 
4.4%
v 62
 
4.4%
y 62
 
4.4%
K 41
 
2.9%
Other values (16) 382
27.3%

period
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
1
2214 
2
1777 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3991
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 2214
55.5%
2 1777
44.5%

Length

2023-05-19T13:56:50.599227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:50.847123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2214
55.5%
2 1777
44.5%

Most occurring characters

ValueCountFrequency (%)
1 2214
55.5%
2 1777
44.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3991
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2214
55.5%
2 1777
44.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2214
55.5%
2 1777
44.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2214
55.5%
2 1777
44.5%

play_pattern
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
Regular Play
1797 
From Throw In
850 
From Free Kick
647 
From Goal Kick
287 
From Corner
 
163
Other values (3)
247 

Length

Max length14
Median length13
Mean length12.655475
Min length11

Characters and Unicode

Total characters50508
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegular Play
2nd rowRegular Play
3rd rowRegular Play
4th rowRegular Play
5th rowFrom Corner

Common Values

ValueCountFrequency (%)
Regular Play 1797
45.0%
From Throw In 850
21.3%
From Free Kick 647
 
16.2%
From Goal Kick 287
 
7.2%
From Corner 163
 
4.1%
From Kick Off 151
 
3.8%
From Keeper 90
 
2.3%
From Counter 6
 
0.2%

Length

2023-05-19T13:56:51.092509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:51.415093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
from 2194
22.1%
regular 1797
18.1%
play 1797
18.1%
kick 1085
10.9%
throw 850
 
8.6%
in 850
 
8.6%
free 647
 
6.5%
goal 287
 
2.9%
corner 163
 
1.6%
off 151
 
1.5%
Other values (2) 96
 
1.0%

Most occurring characters

ValueCountFrequency (%)
5926
 
11.7%
r 5910
 
11.7%
l 3881
 
7.7%
a 3881
 
7.7%
e 3530
 
7.0%
o 3500
 
6.9%
F 2841
 
5.6%
m 2194
 
4.3%
u 1803
 
3.6%
R 1797
 
3.6%
Other values (18) 15245
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34665
68.6%
Uppercase Letter 9917
 
19.6%
Space Separator 5926
 
11.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 5910
17.0%
l 3881
11.2%
a 3881
11.2%
e 3530
10.2%
o 3500
10.1%
m 2194
 
6.3%
u 1803
 
5.2%
y 1797
 
5.2%
g 1797
 
5.2%
k 1085
 
3.1%
Other values (8) 5287
15.3%
Uppercase Letter
ValueCountFrequency (%)
F 2841
28.6%
R 1797
18.1%
P 1797
18.1%
K 1175
11.8%
I 850
 
8.6%
T 850
 
8.6%
G 287
 
2.9%
C 169
 
1.7%
O 151
 
1.5%
Space Separator
ValueCountFrequency (%)
5926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44582
88.3%
Common 5926
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 5910
13.3%
l 3881
 
8.7%
a 3881
 
8.7%
e 3530
 
7.9%
o 3500
 
7.9%
F 2841
 
6.4%
m 2194
 
4.9%
u 1803
 
4.0%
R 1797
 
4.0%
y 1797
 
4.0%
Other values (17) 13448
30.2%
Common
ValueCountFrequency (%)
5926
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5926
 
11.7%
r 5910
 
11.7%
l 3881
 
7.7%
a 3881
 
7.7%
e 3530
 
7.0%
o 3500
 
6.9%
F 2841
 
5.6%
m 2194
 
4.3%
u 1803
 
3.6%
R 1797
 
3.6%
Other values (18) 15245
30.2%

player
Categorical

Distinct33
Distinct (%)0.8%
Missing12
Missing (%)0.3%
Memory size31.3 KiB
Clément Lenglet
292 
Lionel Andrés Messi Cuccittini
282 
Jordi Alba Ramos
274 
Óscar Mingueza García
269 
Sergio Busquets i Burgos
 
258
Other values (28)
2604 

Length

Max length48
Median length30
Mean length20.814275
Min length10

Characters and Unicode

Total characters82820
Distinct characters56
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowLionel Andrés Messi Cuccittini
2nd rowSergio Busquets i Burgos
3rd rowClément Lenglet
4th rowSergio Busquets i Burgos
5th rowClément Lenglet

Common Values

ValueCountFrequency (%)
Clément Lenglet 292
 
7.3%
Lionel Andrés Messi Cuccittini 282
 
7.1%
Jordi Alba Ramos 274
 
6.9%
Óscar Mingueza García 269
 
6.7%
Sergio Busquets i Burgos 258
 
6.5%
Frenkie de Jong 244
 
6.1%
Pedro González López 228
 
5.7%
Luka Modrić 167
 
4.2%
Ronald Federico Araújo da Silva 151
 
3.8%
Vinícius José Paixão de Oliveira Júnior 145
 
3.6%
Other values (23) 1669
41.8%

Length

2023-05-19T13:56:51.753420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 401
 
3.3%
clément 292
 
2.4%
lenglet 292
 
2.4%
andrés 282
 
2.3%
cuccittini 282
 
2.3%
messi 282
 
2.3%
lionel 282
 
2.3%
jordi 274
 
2.2%
alba 274
 
2.2%
ramos 274
 
2.2%
Other values (86) 9397
76.2%

Most occurring characters

ValueCountFrequency (%)
8353
 
10.1%
e 7434
 
9.0%
i 6566
 
7.9%
r 5547
 
6.7%
o 5449
 
6.6%
a 5376
 
6.5%
n 4914
 
5.9%
s 3923
 
4.7%
l 3042
 
3.7%
d 2573
 
3.1%
Other values (46) 29643
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62876
75.9%
Uppercase Letter 11498
 
13.9%
Space Separator 8353
 
10.1%
Dash Punctuation 93
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7434
11.8%
i 6566
10.4%
r 5547
 
8.8%
o 5449
 
8.7%
a 5376
 
8.6%
n 4914
 
7.8%
s 3923
 
6.2%
l 3042
 
4.8%
d 2573
 
4.1%
u 2499
 
4.0%
Other values (22) 15553
24.7%
Uppercase Letter
ValueCountFrequency (%)
M 1223
10.6%
L 1054
 
9.2%
C 1034
 
9.0%
A 1031
 
9.0%
J 909
 
7.9%
S 833
 
7.2%
F 721
 
6.3%
G 672
 
5.8%
B 642
 
5.6%
R 527
 
4.6%
Other values (12) 2852
24.8%
Space Separator
ValueCountFrequency (%)
8353
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74374
89.8%
Common 8446
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7434
 
10.0%
i 6566
 
8.8%
r 5547
 
7.5%
o 5449
 
7.3%
a 5376
 
7.2%
n 4914
 
6.6%
s 3923
 
5.3%
l 3042
 
4.1%
d 2573
 
3.5%
u 2499
 
3.4%
Other values (44) 27051
36.4%
Common
ValueCountFrequency (%)
8353
98.9%
- 93
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79290
95.7%
None 3530
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8353
 
10.5%
e 7434
 
9.4%
i 6566
 
8.3%
r 5547
 
7.0%
o 5449
 
6.9%
a 5376
 
6.8%
n 4914
 
6.2%
s 3923
 
4.9%
l 3042
 
3.8%
d 2573
 
3.2%
Other values (36) 26113
32.9%
None
ValueCountFrequency (%)
é 1135
32.2%
í 504
14.3%
á 458
13.0%
ú 320
 
9.1%
ó 274
 
7.8%
Ó 269
 
7.6%
ã 248
 
7.0%
ć 167
 
4.7%
É 91
 
2.6%
Á 64
 
1.8%

player_id
Real number (ℝ)

Distinct33
Distinct (%)0.8%
Missing12
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean12903.731
Minimum3163
Maximum43728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:52.061306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3163
5-th percentile3804
Q15463
median6379
Q319677
95-th percentile43728
Maximum43728
Range40565
Interquartile range (IQR)14214

Descriptive statistics

Standard deviation12060.565
Coefficient of variation (CV)0.93465721
Kurtosis0.7475112
Mean12903.731
Median Absolute Deviation (MAD)1176
Skewness1.4431258
Sum51343945
Variance1.4545723 × 108
MonotonicityNot monotonic
2023-05-19T13:56:52.366349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
6826 292
 
7.3%
5503 282
 
7.1%
5211 274
 
6.9%
43728 269
 
6.7%
5203 258
 
6.5%
8118 244
 
6.1%
30486 228
 
5.7%
5463 167
 
4.2%
32480 151
 
3.8%
18395 145
 
3.6%
Other values (23) 1669
41.8%
ValueCountFrequency (%)
3163 45
 
1.1%
3509 83
 
2.1%
3804 96
 
2.4%
4447 9
 
0.2%
4926 34
 
0.9%
5200 85
 
2.1%
5202 77
 
1.9%
5203 258
6.5%
5211 274
6.9%
5213 1
 
< 0.1%
ValueCountFrequency (%)
43728 269
6.7%
39073 40
 
1.0%
32480 151
3.8%
30486 228
5.7%
22390 12
 
0.3%
21881 98
 
2.5%
20055 93
 
2.3%
19677 116
2.9%
18395 145
3.6%
13620 91
 
2.3%

position
Categorical

Distinct21
Distinct (%)0.5%
Missing12
Missing (%)0.3%
Memory size31.3 KiB
Left Center Back
369 
Right Center Back
360 
Left Wing Back
274 
Left Center Midfield
258 
Center Defensive Midfield
258 
Other values (16)
2460 

Length

Max length25
Median length20
Mean length16.116612
Min length9

Characters and Unicode

Total characters64128
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowRight Wing
2nd rowRight Defensive Midfield
3rd rowLeft Center Back
4th rowRight Defensive Midfield
5th rowLeft Center Back

Common Values

ValueCountFrequency (%)
Left Center Back 369
 
9.2%
Right Center Back 360
 
9.0%
Left Wing Back 274
 
6.9%
Left Center Midfield 258
 
6.5%
Center Defensive Midfield 258
 
6.5%
Center Forward 250
 
6.3%
Right Center Midfield 237
 
5.9%
Right Defensive Midfield 209
 
5.2%
Center Back 191
 
4.8%
Right Wing Back 187
 
4.7%
Other values (11) 1386
34.7%

Length

2023-05-19T13:56:52.700420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
center 2085
20.3%
back 1626
15.9%
right 1569
15.3%
left 1534
15.0%
midfield 1430
14.0%
wing 795
 
7.8%
defensive 621
 
6.1%
forward 412
 
4.0%
goalkeeper 176
 
1.7%
substitute 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 9526
14.9%
6270
 
9.8%
i 5846
 
9.1%
t 5191
 
8.1%
f 3585
 
5.6%
n 3501
 
5.5%
d 3272
 
5.1%
r 3085
 
4.8%
g 2364
 
3.7%
a 2214
 
3.5%
Other values (21) 19274
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47609
74.2%
Uppercase Letter 10249
 
16.0%
Space Separator 6270
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9526
20.0%
i 5846
12.3%
t 5191
10.9%
f 3585
 
7.5%
n 3501
 
7.4%
d 3272
 
6.9%
r 3085
 
6.5%
g 2364
 
5.0%
a 2214
 
4.7%
k 1802
 
3.8%
Other values (10) 7223
15.2%
Uppercase Letter
ValueCountFrequency (%)
C 2085
20.3%
B 1626
15.9%
R 1569
15.3%
L 1534
15.0%
M 1430
14.0%
W 795
 
7.8%
D 621
 
6.1%
F 412
 
4.0%
G 176
 
1.7%
S 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57858
90.2%
Common 6270
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9526
16.5%
i 5846
 
10.1%
t 5191
 
9.0%
f 3585
 
6.2%
n 3501
 
6.1%
d 3272
 
5.7%
r 3085
 
5.3%
g 2364
 
4.1%
a 2214
 
3.8%
C 2085
 
3.6%
Other values (20) 17189
29.7%
Common
ValueCountFrequency (%)
6270
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9526
14.9%
6270
 
9.8%
i 5846
 
9.1%
t 5191
 
8.1%
f 3585
 
5.6%
n 3501
 
5.5%
d 3272
 
5.1%
r 3085
 
4.8%
g 2364
 
3.7%
a 2214
 
3.5%
Other values (21) 19274
30.1%

possession
Real number (ℝ)

Distinct176
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.505387
Minimum1
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:53.021403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q139
median86
Q3126
95-th percentile163
Maximum176
Range175
Interquartile range (IQR)87

Descriptive statistics

Standard deviation49.821007
Coefficient of variation (CV)0.58956013
Kurtosis-1.1430167
Mean84.505387
Median Absolute Deviation (MAD)42
Skewness0.02919537
Sum337261
Variance2482.1327
MonotonicityNot monotonic
2023-05-19T13:56:53.622591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131 105
 
2.6%
33 97
 
2.4%
65 97
 
2.4%
4 91
 
2.3%
60 83
 
2.1%
86 80
 
2.0%
161 78
 
2.0%
45 76
 
1.9%
61 74
 
1.9%
80 66
 
1.7%
Other values (166) 3144
78.8%
ValueCountFrequency (%)
1 4
 
0.1%
2 50
1.3%
3 11
 
0.3%
4 91
2.3%
5 64
1.6%
6 47
1.2%
7 6
 
0.2%
8 15
 
0.4%
9 13
 
0.3%
10 3
 
0.1%
ValueCountFrequency (%)
176 20
0.5%
175 13
0.3%
174 5
 
0.1%
173 32
0.8%
172 8
 
0.2%
171 23
0.6%
170 2
 
0.1%
169 2
 
0.1%
168 29
0.7%
167 17
0.4%

possession_team
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
Barcelona
2741 
Real Madrid
1250 

Length

Max length11
Median length9
Mean length9.6264094
Min length9

Characters and Unicode

Total characters38419
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReal Madrid
2nd rowReal Madrid
3rd rowReal Madrid
4th rowReal Madrid
5th rowBarcelona

Common Values

ValueCountFrequency (%)
Barcelona 2741
68.7%
Real Madrid 1250
31.3%

Length

2023-05-19T13:56:53.950992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:54.373387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
barcelona 2741
52.3%
real 1250
23.9%
madrid 1250
23.9%

Most occurring characters

ValueCountFrequency (%)
a 7982
20.8%
r 3991
10.4%
e 3991
10.4%
l 3991
10.4%
B 2741
 
7.1%
c 2741
 
7.1%
o 2741
 
7.1%
n 2741
 
7.1%
d 2500
 
6.5%
R 1250
 
3.3%
Other values (3) 3750
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31928
83.1%
Uppercase Letter 5241
 
13.6%
Space Separator 1250
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7982
25.0%
r 3991
12.5%
e 3991
12.5%
l 3991
12.5%
c 2741
 
8.6%
o 2741
 
8.6%
n 2741
 
8.6%
d 2500
 
7.8%
i 1250
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
B 2741
52.3%
R 1250
23.9%
M 1250
23.9%
Space Separator
ValueCountFrequency (%)
1250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37169
96.7%
Common 1250
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7982
21.5%
r 3991
10.7%
e 3991
10.7%
l 3991
10.7%
B 2741
 
7.4%
c 2741
 
7.4%
o 2741
 
7.4%
n 2741
 
7.4%
d 2500
 
6.7%
R 1250
 
3.4%
Other values (2) 2500
 
6.7%
Common
ValueCountFrequency (%)
1250
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7982
20.8%
r 3991
10.4%
e 3991
10.4%
l 3991
10.4%
B 2741
 
7.1%
c 2741
 
7.1%
o 2741
 
7.1%
n 2741
 
7.1%
d 2500
 
6.5%
R 1250
 
3.3%
Other values (3) 3750
9.8%

possession_team_id
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
217
2741 
220
1250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11973
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row220
2nd row220
3rd row220
4th row220
5th row217

Common Values

ValueCountFrequency (%)
217 2741
68.7%
220 1250
31.3%

Length

2023-05-19T13:56:54.721584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:54.964609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
217 2741
68.7%
220 1250
31.3%

Most occurring characters

ValueCountFrequency (%)
2 5241
43.8%
1 2741
22.9%
7 2741
22.9%
0 1250
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5241
43.8%
1 2741
22.9%
7 2741
22.9%
0 1250
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 11973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5241
43.8%
1 2741
22.9%
7 2741
22.9%
0 1250
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5241
43.8%
1 2741
22.9%
7 2741
22.9%
0 1250
 
10.4%

related_events
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing135
Missing (%)3.4%
Memory size31.3 KiB

second
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.666249
Minimum0
Maximum59
Zeros67
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:55.197796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median28
Q344
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.448711
Coefficient of variation (CV)0.60868482
Kurtosis-1.2173777
Mean28.666249
Median Absolute Deviation (MAD)15
Skewness0.087814016
Sum114407
Variance304.4575
MonotonicityNot monotonic
2023-05-19T13:56:55.563319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 87
 
2.2%
19 87
 
2.2%
5 86
 
2.2%
21 84
 
2.1%
7 84
 
2.1%
6 81
 
2.0%
22 81
 
2.0%
56 80
 
2.0%
20 76
 
1.9%
9 76
 
1.9%
Other values (50) 3169
79.4%
ValueCountFrequency (%)
0 67
1.7%
1 62
1.6%
2 73
1.8%
3 58
1.5%
4 73
1.8%
5 86
2.2%
6 81
2.0%
7 84
2.1%
8 73
1.8%
9 76
1.9%
ValueCountFrequency (%)
59 61
1.5%
58 67
1.7%
57 73
1.8%
56 80
2.0%
55 59
1.5%
54 57
1.4%
53 66
1.7%
52 63
1.6%
51 59
1.5%
50 64
1.6%

shot_aerial_won
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3990
Missing (%)> 99.9%
Memory size31.3 KiB
True
 
1
(Missing)
3990 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 3990
> 99.9%
2023-05-19T13:56:55.953207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_body_part
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)9.4%
Missing3959
Missing (%)99.2%
Memory size31.3 KiB
Right Foot
16 
Left Foot
12 
Head
4 

Length

Max length10
Median length9.5
Mean length8.875
Min length4

Characters and Unicode

Total characters284
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRight Foot
2nd rowRight Foot
3rd rowHead
4th rowLeft Foot
5th rowRight Foot

Common Values

ValueCountFrequency (%)
Right Foot 16
 
0.4%
Left Foot 12
 
0.3%
Head 4
 
0.1%
(Missing) 3959
99.2%

Length

2023-05-19T13:56:56.133381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:56.461256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
foot 28
46.7%
right 16
26.7%
left 12
20.0%
head 4
 
6.7%

Most occurring characters

ValueCountFrequency (%)
t 56
19.7%
o 56
19.7%
28
9.9%
F 28
9.9%
R 16
 
5.6%
i 16
 
5.6%
g 16
 
5.6%
h 16
 
5.6%
e 16
 
5.6%
L 12
 
4.2%
Other values (4) 24
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 196
69.0%
Uppercase Letter 60
 
21.1%
Space Separator 28
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 56
28.6%
o 56
28.6%
i 16
 
8.2%
g 16
 
8.2%
h 16
 
8.2%
e 16
 
8.2%
f 12
 
6.1%
a 4
 
2.0%
d 4
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
F 28
46.7%
R 16
26.7%
L 12
20.0%
H 4
 
6.7%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 256
90.1%
Common 28
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 56
21.9%
o 56
21.9%
F 28
10.9%
R 16
 
6.2%
i 16
 
6.2%
g 16
 
6.2%
h 16
 
6.2%
e 16
 
6.2%
L 12
 
4.7%
f 12
 
4.7%
Other values (3) 12
 
4.7%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 56
19.7%
o 56
19.7%
28
9.9%
F 28
9.9%
R 16
 
5.6%
i 16
 
5.6%
g 16
 
5.6%
h 16
 
5.6%
e 16
 
5.6%
L 12
 
4.2%
Other values (4) 24
8.5%

shot_deflected
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3990
Missing (%)> 99.9%
Memory size31.3 KiB
True
 
1
(Missing)
3990 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 3990
> 99.9%
2023-05-19T13:56:56.715450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_end_location
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3959
Missing (%)99.2%
Memory size31.3 KiB

shot_first_time
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)7.1%
Missing3977
Missing (%)99.6%
Memory size31.3 KiB
True
 
14
(Missing)
3977 
ValueCountFrequency (%)
True 14
 
0.4%
(Missing) 3977
99.6%
2023-05-19T13:56:56.973911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

shot_freeze_frame
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3959
Missing (%)99.2%
Memory size31.3 KiB

shot_key_pass_id
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct20
Distinct (%)100.0%
Missing3971
Missing (%)99.5%
Memory size31.3 KiB
f9aa1da0-daad-4d4a-a150-403679ea0af2
 
1
bd902c8f-acd1-4362-a935-758c6c86d547
 
1
c9426a1f-7532-4cdb-bb2d-232974a7c099
 
1
cf4fcdfd-9f89-46b9-80ff-fcf7ac5902e4
 
1
389691e5-735d-4d89-be45-aff0c86b86a0
 
1
Other values (15)
15 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters720
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row3ba430bb-0f11-4ee2-a3c9-b4e2bb106bd6
2nd row79a8173d-31c9-4621-aee6-70933a03fc8e
3rd row27685bfe-e4a3-4669-995b-414651bfd38e
4th rowa659922f-8a36-43e0-90a0-4d96db89f3e3
5th row903f6d20-20d1-4a7f-b7aa-4c234953b1e4

Common Values

ValueCountFrequency (%)
f9aa1da0-daad-4d4a-a150-403679ea0af2 1
 
< 0.1%
bd902c8f-acd1-4362-a935-758c6c86d547 1
 
< 0.1%
c9426a1f-7532-4cdb-bb2d-232974a7c099 1
 
< 0.1%
cf4fcdfd-9f89-46b9-80ff-fcf7ac5902e4 1
 
< 0.1%
389691e5-735d-4d89-be45-aff0c86b86a0 1
 
< 0.1%
db05d430-d3e6-4d58-b39f-03d10454e851 1
 
< 0.1%
33bba633-1e10-4738-9cac-fc415d0b3b4b 1
 
< 0.1%
5d4f09ed-8334-4226-a1f7-7c41e931bf4f 1
 
< 0.1%
2a13be74-fbb7-4af3-ab4a-4f57e6dce1bb 1
 
< 0.1%
a047d916-6903-4ebd-92d6-c728735d0f7d 1
 
< 0.1%
Other values (10) 10
 
0.3%
(Missing) 3971
99.5%

Length

2023-05-19T13:56:57.180611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
f9aa1da0-daad-4d4a-a150-403679ea0af2 1
 
5.0%
bd902c8f-acd1-4362-a935-758c6c86d547 1
 
5.0%
79a8173d-31c9-4621-aee6-70933a03fc8e 1
 
5.0%
27685bfe-e4a3-4669-995b-414651bfd38e 1
 
5.0%
a659922f-8a36-43e0-90a0-4d96db89f3e3 1
 
5.0%
903f6d20-20d1-4a7f-b7aa-4c234953b1e4 1
 
5.0%
93ce8980-bc89-4d13-87b2-a3804fd29821 1
 
5.0%
22a4014b-11fb-4856-a54f-54128d729c09 1
 
5.0%
0a67afa9-13c1-45bf-9065-7119282c7332 1
 
5.0%
a3ed1cd4-4aae-475d-850d-ade7b9e60208 1
 
5.0%
Other values (10) 10
50.0%

Most occurring characters

ValueCountFrequency (%)
- 80
 
11.1%
4 58
 
8.1%
3 50
 
6.9%
a 48
 
6.7%
9 48
 
6.7%
d 44
 
6.1%
b 42
 
5.8%
f 40
 
5.6%
0 40
 
5.6%
2 39
 
5.4%
Other values (7) 231
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 405
56.2%
Lowercase Letter 235
32.6%
Dash Punctuation 80
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 58
14.3%
3 50
12.3%
9 48
11.9%
0 40
9.9%
2 39
9.6%
1 39
9.6%
6 36
8.9%
5 33
8.1%
7 31
7.7%
8 31
7.7%
Lowercase Letter
ValueCountFrequency (%)
a 48
20.4%
d 44
18.7%
b 42
17.9%
f 40
17.0%
e 32
13.6%
c 29
12.3%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 485
67.4%
Latin 235
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 80
16.5%
4 58
12.0%
3 50
10.3%
9 48
9.9%
0 40
8.2%
2 39
8.0%
1 39
8.0%
6 36
7.4%
5 33
6.8%
7 31
 
6.4%
Latin
ValueCountFrequency (%)
a 48
20.4%
d 44
18.7%
b 42
17.9%
f 40
17.0%
e 32
13.6%
c 29
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 80
 
11.1%
4 58
 
8.1%
3 50
 
6.9%
a 48
 
6.7%
9 48
 
6.7%
d 44
 
6.1%
b 42
 
5.8%
f 40
 
5.6%
0 40
 
5.6%
2 39
 
5.4%
Other values (7) 231
32.1%

shot_outcome
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)18.8%
Missing3959
Missing (%)99.2%
Memory size31.3 KiB
Blocked
12 
Off T
9 
Saved
4 
Goal
3 
Wayward
3 

Length

Max length7
Median length5
Mean length5.8125
Min length4

Characters and Unicode

Total characters186
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st rowOff T
2nd rowGoal
3rd rowWayward
4th rowBlocked
5th rowOff T

Common Values

ValueCountFrequency (%)
Blocked 12
 
0.3%
Off T 9
 
0.2%
Saved 4
 
0.1%
Goal 3
 
0.1%
Wayward 3
 
0.1%
Post 1
 
< 0.1%
(Missing) 3959
99.2%

Length

2023-05-19T13:56:57.466216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:57.766929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
blocked 12
29.3%
off 9
22.0%
t 9
22.0%
saved 4
 
9.8%
goal 3
 
7.3%
wayward 3
 
7.3%
post 1
 
2.4%

Most occurring characters

ValueCountFrequency (%)
d 19
10.2%
f 18
9.7%
o 16
 
8.6%
e 16
 
8.6%
l 15
 
8.1%
a 13
 
7.0%
B 12
 
6.5%
c 12
 
6.5%
k 12
 
6.5%
O 9
 
4.8%
Other values (12) 44
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 136
73.1%
Uppercase Letter 41
 
22.0%
Space Separator 9
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 19
14.0%
f 18
13.2%
o 16
11.8%
e 16
11.8%
l 15
11.0%
a 13
9.6%
c 12
8.8%
k 12
8.8%
v 4
 
2.9%
y 3
 
2.2%
Other values (4) 8
5.9%
Uppercase Letter
ValueCountFrequency (%)
B 12
29.3%
O 9
22.0%
T 9
22.0%
S 4
 
9.8%
G 3
 
7.3%
W 3
 
7.3%
P 1
 
2.4%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 177
95.2%
Common 9
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 19
10.7%
f 18
10.2%
o 16
9.0%
e 16
9.0%
l 15
8.5%
a 13
 
7.3%
B 12
 
6.8%
c 12
 
6.8%
k 12
 
6.8%
O 9
 
5.1%
Other values (11) 35
19.8%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 19
10.2%
f 18
9.7%
o 16
 
8.6%
e 16
 
8.6%
l 15
 
8.1%
a 13
 
7.0%
B 12
 
6.5%
c 12
 
6.5%
k 12
 
6.5%
O 9
 
4.8%
Other values (12) 44
23.7%

shot_statsbomb_xg
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing3959
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean0.088007162
Minimum0.012394485
Maximum0.3197491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-05-19T13:56:58.041877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.012394485
5-th percentile0.01999547
Q10.044441468
median0.063922987
Q30.11210766
95-th percentile0.2304789
Maximum0.3197491
Range0.30735461
Interquartile range (IQR)0.067666196

Descriptive statistics

Standard deviation0.073827478
Coefficient of variation (CV)0.83888033
Kurtosis3.7963391
Mean0.088007162
Median Absolute Deviation (MAD)0.023109465
Skewness1.9213306
Sum2.8162292
Variance0.0054504964
MonotonicityNot monotonic
2023-05-19T13:56:58.375947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.104605675 1
 
< 0.1%
0.057932165 1
 
< 0.1%
0.070164755 1
 
< 0.1%
0.046457313 1
 
< 0.1%
0.05946907 1
 
< 0.1%
0.06397319 1
 
< 0.1%
0.04911825 1
 
< 0.1%
0.045265324 1
 
< 0.1%
0.15727945 1
 
< 0.1%
0.063872784 1
 
< 0.1%
Other values (22) 22
 
0.6%
(Missing) 3959
99.2%
ValueCountFrequency (%)
0.012394485 1
< 0.1%
0.01567179 1
< 0.1%
0.023533026 1
< 0.1%
0.027073873 1
< 0.1%
0.03774876 1
< 0.1%
0.040328264 1
< 0.1%
0.04129878 1
< 0.1%
0.0419699 1
< 0.1%
0.045265324 1
< 0.1%
0.04573942 1
< 0.1%
ValueCountFrequency (%)
0.3197491 1
< 0.1%
0.3056659 1
< 0.1%
0.16896226 1
< 0.1%
0.16597773 1
< 0.1%
0.15830496 1
< 0.1%
0.15727945 1
< 0.1%
0.1361084 1
< 0.1%
0.13461363 1
< 0.1%
0.104605675 1
< 0.1%
0.09082136 1
< 0.1%

shot_technique
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)12.5%
Missing3959
Missing (%)99.2%
Memory size31.3 KiB
Normal
21 
Half Volley
7 
Volley
3 
Backheel
 
1

Length

Max length11
Median length6
Mean length7.15625
Min length6

Characters and Unicode

Total characters229
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st rowNormal
2nd rowBackheel
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 21
 
0.5%
Half Volley 7
 
0.2%
Volley 3
 
0.1%
Backheel 1
 
< 0.1%
(Missing) 3959
99.2%

Length

2023-05-19T13:56:58.736059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:58.986602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 21
53.8%
volley 10
25.6%
half 7
 
17.9%
backheel 1
 
2.6%

Most occurring characters

ValueCountFrequency (%)
l 49
21.4%
o 31
13.5%
a 29
12.7%
N 21
9.2%
r 21
9.2%
m 21
9.2%
e 12
 
5.2%
V 10
 
4.4%
y 10
 
4.4%
H 7
 
3.1%
Other values (6) 18
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 183
79.9%
Uppercase Letter 39
 
17.0%
Space Separator 7
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 49
26.8%
o 31
16.9%
a 29
15.8%
r 21
11.5%
m 21
11.5%
e 12
 
6.6%
y 10
 
5.5%
f 7
 
3.8%
c 1
 
0.5%
k 1
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
N 21
53.8%
V 10
25.6%
H 7
 
17.9%
B 1
 
2.6%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222
96.9%
Common 7
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 49
22.1%
o 31
14.0%
a 29
13.1%
N 21
9.5%
r 21
9.5%
m 21
9.5%
e 12
 
5.4%
V 10
 
4.5%
y 10
 
4.5%
H 7
 
3.2%
Other values (5) 11
 
5.0%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 49
21.4%
o 31
13.5%
a 29
12.7%
N 21
9.2%
r 21
9.2%
m 21
9.2%
e 12
 
5.2%
V 10
 
4.4%
y 10
 
4.4%
H 7
 
3.1%
Other values (6) 18
 
7.9%

shot_type
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)6.2%
Missing3959
Missing (%)99.2%
Memory size31.3 KiB
Open Play
27 
Free Kick
5 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters288
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen Play
2nd rowOpen Play
3rd rowOpen Play
4th rowOpen Play
5th rowFree Kick

Common Values

ValueCountFrequency (%)
Open Play 27
 
0.7%
Free Kick 5
 
0.1%
(Missing) 3959
99.2%

Length

2023-05-19T13:56:59.234760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:56:59.562376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
open 27
42.2%
play 27
42.2%
free 5
 
7.8%
kick 5
 
7.8%

Most occurring characters

ValueCountFrequency (%)
e 37
12.8%
32
11.1%
O 27
9.4%
p 27
9.4%
n 27
9.4%
P 27
9.4%
l 27
9.4%
a 27
9.4%
y 27
9.4%
F 5
 
1.7%
Other values (5) 25
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 192
66.7%
Uppercase Letter 64
 
22.2%
Space Separator 32
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 37
19.3%
p 27
14.1%
n 27
14.1%
l 27
14.1%
a 27
14.1%
y 27
14.1%
r 5
 
2.6%
i 5
 
2.6%
c 5
 
2.6%
k 5
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
O 27
42.2%
P 27
42.2%
F 5
 
7.8%
K 5
 
7.8%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 256
88.9%
Common 32
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 37
14.5%
O 27
10.5%
p 27
10.5%
n 27
10.5%
P 27
10.5%
l 27
10.5%
a 27
10.5%
y 27
10.5%
F 5
 
2.0%
r 5
 
2.0%
Other values (4) 20
7.8%
Common
ValueCountFrequency (%)
32
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 37
12.8%
32
11.1%
O 27
9.4%
p 27
9.4%
n 27
9.4%
P 27
9.4%
l 27
9.4%
a 27
9.4%
y 27
9.4%
F 5
 
1.7%
Other values (5) 25
8.7%

substitution_outcome
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)20.0%
Missing3981
Missing (%)99.7%
Memory size31.3 KiB
Tactical
9 
Injury
1 

Length

Max length8
Median length8
Mean length7.8
Min length6

Characters and Unicode

Total characters78
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st rowInjury
2nd rowTactical
3rd rowTactical
4th rowTactical
5th rowTactical

Common Values

ValueCountFrequency (%)
Tactical 9
 
0.2%
Injury 1
 
< 0.1%
(Missing) 3981
99.7%

Length

2023-05-19T13:56:59.912653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:57:00.398599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tactical 9
90.0%
injury 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
a 18
23.1%
c 18
23.1%
T 9
11.5%
t 9
11.5%
i 9
11.5%
l 9
11.5%
I 1
 
1.3%
n 1
 
1.3%
j 1
 
1.3%
u 1
 
1.3%
Other values (2) 2
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 68
87.2%
Uppercase Letter 10
 
12.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 18
26.5%
c 18
26.5%
t 9
13.2%
i 9
13.2%
l 9
13.2%
n 1
 
1.5%
j 1
 
1.5%
u 1
 
1.5%
r 1
 
1.5%
y 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
T 9
90.0%
I 1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 18
23.1%
c 18
23.1%
T 9
11.5%
t 9
11.5%
i 9
11.5%
l 9
11.5%
I 1
 
1.3%
n 1
 
1.3%
j 1
 
1.3%
u 1
 
1.3%
Other values (2) 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 18
23.1%
c 18
23.1%
T 9
11.5%
t 9
11.5%
i 9
11.5%
l 9
11.5%
I 1
 
1.3%
n 1
 
1.3%
j 1
 
1.3%
u 1
 
1.3%
Other values (2) 2
 
2.6%

substitution_replacement
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct10
Distinct (%)100.0%
Missing3981
Missing (%)99.7%
Memory size31.3 KiB
Álvaro Odriozola Arzallus
1 
Antoine Griezmann
1 
Marco Asensio Willemsen
1 
Sergi Roberto Carnicer
1 
Moriba Kourouma Kourouma
1 
Other values (5)
5 

Length

Max length48
Median length24.5
Mean length26.7
Min length17

Characters and Unicode

Total characters267
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st rowÁlvaro Odriozola Arzallus
2nd rowAntoine Griezmann
3rd rowMarco Asensio Willemsen
4th rowSergi Roberto Carnicer
5th rowMoriba Kourouma Kourouma

Common Values

ValueCountFrequency (%)
Álvaro Odriozola Arzallus 1
 
< 0.1%
Antoine Griezmann 1
 
< 0.1%
Marco Asensio Willemsen 1
 
< 0.1%
Sergi Roberto Carnicer 1
 
< 0.1%
Moriba Kourouma Kourouma 1
 
< 0.1%
Mariano Díaz Mejía 1
 
< 0.1%
Francisco Román Alarcón Suárez 1
 
< 0.1%
Marcelo Vieira da Silva Júnior 1
 
< 0.1%
Martin Braithwaite Christensen 1
 
< 0.1%
Francisco António Machado Mota de Castro Trincão 1
 
< 0.1%
(Missing) 3981
99.7%

Length

2023-05-19T13:57:00.679252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:57:01.061238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
francisco 2
 
5.6%
kourouma 2
 
5.6%
álvaro 1
 
2.8%
alarcón 1
 
2.8%
marcelo 1
 
2.8%
vieira 1
 
2.8%
da 1
 
2.8%
silva 1
 
2.8%
júnior 1
 
2.8%
martin 1
 
2.8%
Other values (24) 24
66.7%

Most occurring characters

ValueCountFrequency (%)
a 27
 
10.1%
26
 
9.7%
r 25
 
9.4%
o 24
 
9.0%
i 21
 
7.9%
n 19
 
7.1%
e 16
 
6.0%
c 10
 
3.7%
t 9
 
3.4%
l 9
 
3.4%
Other values (32) 81
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 207
77.5%
Uppercase Letter 34
 
12.7%
Space Separator 26
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 27
13.0%
r 25
12.1%
o 24
11.6%
i 21
10.1%
n 19
9.2%
e 16
7.7%
c 10
 
4.8%
t 9
 
4.3%
l 9
 
4.3%
s 9
 
4.3%
Other values (15) 38
18.4%
Uppercase Letter
ValueCountFrequency (%)
M 8
23.5%
A 5
14.7%
C 3
 
8.8%
S 3
 
8.8%
R 2
 
5.9%
K 2
 
5.9%
F 2
 
5.9%
Á 1
 
2.9%
B 1
 
2.9%
J 1
 
2.9%
Other values (6) 6
17.6%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 241
90.3%
Common 26
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 27
 
11.2%
r 25
 
10.4%
o 24
 
10.0%
i 21
 
8.7%
n 19
 
7.9%
e 16
 
6.6%
c 10
 
4.1%
t 9
 
3.7%
l 9
 
3.7%
s 9
 
3.7%
Other values (31) 72
29.9%
Common
ValueCountFrequency (%)
26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258
96.6%
None 9
 
3.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 27
 
10.5%
26
 
10.1%
r 25
 
9.7%
o 24
 
9.3%
i 21
 
8.1%
n 19
 
7.4%
e 16
 
6.2%
c 10
 
3.9%
t 9
 
3.5%
l 9
 
3.5%
Other values (26) 72
27.9%
None
ValueCountFrequency (%)
ó 2
22.2%
í 2
22.2%
á 2
22.2%
ú 1
11.1%
Á 1
11.1%
ã 1
11.1%

tactics
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3987
Missing (%)99.9%
Memory size31.3 KiB

team
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
Barcelona
2531 
Real Madrid
1460 

Length

Max length11
Median length9
Mean length9.7316462
Min length9

Characters and Unicode

Total characters38839
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReal Madrid
2nd rowBarcelona
3rd rowBarcelona
4th rowReal Madrid
5th rowBarcelona

Common Values

ValueCountFrequency (%)
Barcelona 2531
63.4%
Real Madrid 1460
36.6%

Length

2023-05-19T13:57:01.568803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-19T13:57:01.989338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
barcelona 2531
46.4%
real 1460
26.8%
madrid 1460
26.8%

Most occurring characters

ValueCountFrequency (%)
a 7982
20.6%
r 3991
10.3%
e 3991
10.3%
l 3991
10.3%
d 2920
 
7.5%
B 2531
 
6.5%
c 2531
 
6.5%
o 2531
 
6.5%
n 2531
 
6.5%
R 1460
 
3.8%
Other values (3) 4380
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31928
82.2%
Uppercase Letter 5451
 
14.0%
Space Separator 1460
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7982
25.0%
r 3991
12.5%
e 3991
12.5%
l 3991
12.5%
d 2920
 
9.1%
c 2531
 
7.9%
o 2531
 
7.9%
n 2531
 
7.9%
i 1460
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 2531
46.4%
R 1460
26.8%
M 1460
26.8%
Space Separator
ValueCountFrequency (%)
1460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37379
96.2%
Common 1460
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7982
21.4%
r 3991
10.7%
e 3991
10.7%
l 3991
10.7%
d 2920
 
7.8%
B 2531
 
6.8%
c 2531
 
6.8%
o 2531
 
6.8%
n 2531
 
6.8%
R 1460
 
3.9%
Other values (2) 2920
 
7.8%
Common
ValueCountFrequency (%)
1460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7982
20.6%
r 3991
10.3%
e 3991
10.3%
l 3991
10.3%
d 2920
 
7.5%
B 2531
 
6.5%
c 2531
 
6.5%
o 2531
 
6.5%
n 2531
 
6.5%
R 1460
 
3.8%
Other values (3) 4380
11.3%

timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct2776
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
00:00:00.000
 
7
00:17:52.168
 
3
00:20:15.159
 
3
00:39:20.868
 
3
00:24:16.389
 
3
Other values (2771)
3972 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters47892
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1644 ?
Unique (%)41.2%

Sample

1st row00:00:00.000
2nd row00:00:00.000
3rd row00:00:00.000
4th row00:00:00.000
5th row00:00:00.000

Common Values

ValueCountFrequency (%)
00:00:00.000 7
 
0.2%
00:17:52.168 3
 
0.1%
00:20:15.159 3
 
0.1%
00:39:20.868 3
 
0.1%
00:24:16.389 3
 
0.1%
00:24:22.061 3
 
0.1%
00:25:39.160 3
 
0.1%
00:29:27.992 3
 
0.1%
00:31:08.173 3
 
0.1%
00:32:47.067 3
 
0.1%
Other values (2766) 3957
99.1%

Length

2023-05-19T13:57:02.297522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.000 7
 
0.2%
00:30:13.569 3
 
0.1%
00:03:28.806 3
 
0.1%
00:08:26.225 3
 
0.1%
00:00:19.376 3
 
0.1%
00:08:38.777 3
 
0.1%
00:09:21.074 3
 
0.1%
00:09:23.399 3
 
0.1%
00:09:12.977 3
 
0.1%
00:09:38.327 3
 
0.1%
Other values (2766) 3957
99.1%

Most occurring characters

ValueCountFrequency (%)
0 12019
25.1%
: 7982
16.7%
. 3991
 
8.3%
1 3566
 
7.4%
2 3399
 
7.1%
3 3331
 
7.0%
4 3077
 
6.4%
5 2754
 
5.8%
7 2024
 
4.2%
6 1987
 
4.1%
Other values (2) 3762
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35919
75.0%
Other Punctuation 11973
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12019
33.5%
1 3566
 
9.9%
2 3399
 
9.5%
3 3331
 
9.3%
4 3077
 
8.6%
5 2754
 
7.7%
7 2024
 
5.6%
6 1987
 
5.5%
9 1961
 
5.5%
8 1801
 
5.0%
Other Punctuation
ValueCountFrequency (%)
: 7982
66.7%
. 3991
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 47892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12019
25.1%
: 7982
16.7%
. 3991
 
8.3%
1 3566
 
7.4%
2 3399
 
7.1%
3 3331
 
7.0%
4 3077
 
6.4%
5 2754
 
5.8%
7 2024
 
4.2%
6 1987
 
4.1%
Other values (2) 3762
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12019
25.1%
: 7982
16.7%
. 3991
 
8.3%
1 3566
 
7.4%
2 3399
 
7.1%
3 3331
 
7.0%
4 3077
 
6.4%
5 2754
 
5.8%
7 2024
 
4.2%
6 1987
 
4.1%
Other values (2) 3762
 
7.9%

type
Categorical

Distinct24
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size31.3 KiB
Pass
1103 
Ball Receipt*
1043 
Carry
971 
Pressure
362 
Ball Recovery
 
99
Other values (19)
413 

Length

Max length15
Median length14
Mean length7.654222
Min length4

Characters and Unicode

Total characters30548
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowStarting XI
2nd rowStarting XI
3rd rowHalf Start
4th rowHalf Start
5th rowHalf Start

Common Values

ValueCountFrequency (%)
Pass 1103
27.6%
Ball Receipt* 1043
26.1%
Carry 971
24.3%
Pressure 362
 
9.1%
Ball Recovery 99
 
2.5%
Duel 51
 
1.3%
Block 47
 
1.2%
Dribble 41
 
1.0%
Goal Keeper 35
 
0.9%
Shot 32
 
0.8%
Other values (14) 207
 
5.2%

Length

2023-05-19T13:57:02.788030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ball 1142
21.7%
pass 1103
20.9%
receipt 1043
19.8%
carry 971
18.4%
pressure 362
 
6.9%
recovery 99
 
1.9%
foul 57
 
1.1%
duel 51
 
1.0%
block 47
 
0.9%
dribble 41
 
0.8%
Other values (23) 353
 
6.7%

Most occurring characters

ValueCountFrequency (%)
e 3412
 
11.2%
a 3367
 
11.0%
s 3097
 
10.1%
r 2954
 
9.7%
l 2609
 
8.5%
P 1494
 
4.9%
1278
 
4.2%
t 1272
 
4.2%
c 1268
 
4.2%
i 1234
 
4.0%
Other values (30) 8563
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22956
75.1%
Uppercase Letter 5271
 
17.3%
Space Separator 1278
 
4.2%
Other Punctuation 1043
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3412
14.9%
a 3367
14.7%
s 3097
13.5%
r 2954
12.9%
l 2609
11.4%
t 1272
 
5.5%
c 1268
 
5.5%
i 1234
 
5.4%
p 1121
 
4.9%
y 1071
 
4.7%
Other values (12) 1551
6.8%
Uppercase Letter
ValueCountFrequency (%)
P 1494
28.3%
B 1193
22.6%
R 1142
21.7%
C 1032
19.6%
D 142
 
2.7%
F 57
 
1.1%
S 51
 
1.0%
K 35
 
0.7%
G 35
 
0.7%
W 28
 
0.5%
Other values (6) 62
 
1.2%
Space Separator
ValueCountFrequency (%)
1278
100.0%
Other Punctuation
ValueCountFrequency (%)
* 1043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28227
92.4%
Common 2321
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3412
12.1%
a 3367
11.9%
s 3097
11.0%
r 2954
10.5%
l 2609
9.2%
P 1494
 
5.3%
t 1272
 
4.5%
c 1268
 
4.5%
i 1234
 
4.4%
B 1193
 
4.2%
Other values (28) 6327
22.4%
Common
ValueCountFrequency (%)
1278
55.1%
* 1043
44.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3412
 
11.2%
a 3367
 
11.0%
s 3097
 
10.1%
r 2954
 
9.7%
l 2609
 
8.5%
P 1494
 
4.9%
1278
 
4.2%
t 1272
 
4.2%
c 1268
 
4.2%
i 1234
 
4.0%
Other values (30) 8563
28.0%

under_pressure
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing3203
Missing (%)80.3%
Memory size31.3 KiB
True
788 
(Missing)
3203 
ValueCountFrequency (%)
True 788
 
19.7%
(Missing) 3203
80.3%
2023-05-19T13:57:03.123900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Interactions

2023-05-19T13:56:17.763957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:00.958028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:02.933571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:05.351480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:07.461991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:09.560868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:11.638527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:13.797948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:15.898740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:18.304794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:01.143171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:03.394280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:05.591983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:07.652946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:09.769908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:11.882372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:14.031011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:16.114243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:18.447494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:01.379709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:03.589720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:05.790303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:07.918736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:09.985652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:12.212090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:14.244842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:16.310748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:18.675505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:01.601358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:03.782507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:06.015194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:08.114550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:10.228649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:12.453691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:14.493954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:16.539618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:18.961184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:01.807124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:03.990557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:06.311140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:08.367091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:10.448201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:12.699823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:14.701146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:16.743618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:19.251094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:02.005125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:04.277198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:06.488186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:08.595042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:10.631582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:12.890632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:14.932460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:16.902727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:19.412788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:02.332657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:04.607351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:06.851299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:08.806824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:11.035331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:13.081196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:15.256737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:17.115048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:19.582070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:02.553140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:04.812402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:07.064622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:09.029827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:11.239288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:13.366202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:15.457006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:17.384795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:19.778573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:02.750849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:05.019045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:07.302086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:09.325570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:11.475525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:13.548310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:15.693013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-05-19T13:56:17.545620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-05-19T13:57:03.842086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
durationdf_indexminutepass_anglepass_lengthplayer_idpossessionsecondshot_statsbomb_xgclearance_body_partdribble_outcomeduel_outcomeduel_typefoul_committed_cardfoul_committed_typegoalkeeper_body_partgoalkeeper_outcomegoalkeeper_positiongoalkeeper_techniquegoalkeeper_typeinterception_outcomepass_assisted_shot_idpass_body_partpass_heightpass_outcomepass_recipientpass_techniquepass_typeperiodplay_patternplayerpositionpossession_teampossession_team_idshot_body_partshot_key_pass_idshot_outcomeshot_techniqueshot_typesubstitution_outcomesubstitution_replacementteamtype
duration1.0000.0260.0260.0000.7050.0410.0270.008-0.2901.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.2960.3080.1200.2150.2120.2080.0580.0000.0560.0500.0130.0130.0001.0000.3940.0000.0001.0001.0000.0780.082
df_index0.0261.0000.9990.0250.0750.0211.000-0.002-0.1500.1860.0000.0000.1830.0000.0001.0000.0000.1721.0000.3220.0001.0000.0360.1090.1330.2120.0750.1090.9470.3010.2340.1770.2150.2150.0001.0000.0820.0000.1610.8661.0000.1160.037
minute0.0260.9991.0000.0250.0750.0210.999-0.015-0.1450.1460.0000.0000.0880.0001.0001.0000.0000.2391.0000.1210.0001.0000.0580.1010.0990.2190.0710.0700.9300.2890.2450.1780.1960.1960.1341.0000.1590.2010.2440.3541.0000.0930.025
pass_angle0.0000.0250.0251.0000.046-0.1390.0250.036NaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0690.1420.0000.2600.5460.2320.0320.0610.1950.1710.1110.1110.0000.0000.0000.0000.0000.0000.0000.1461.000
pass_length0.7050.0750.0750.0461.000-0.0490.0750.041NaN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.2690.3730.1110.1740.0000.2700.0310.0190.1880.1260.1430.1430.0000.0000.0000.0000.0000.0000.0000.1911.000
player_id0.0410.0210.021-0.139-0.0491.0000.022-0.001-0.0390.0000.0520.0000.0000.0000.0000.0000.0000.2590.0000.0000.0001.0000.1510.1070.0000.2360.0000.2020.1770.0750.9970.7640.3070.3070.1691.0000.0000.1450.0000.0001.0000.5020.071
possession0.0271.0000.9990.0250.0750.0221.000-0.003-0.1470.1250.0000.0000.2430.0001.0001.0000.0000.2780.0000.0000.0001.0000.0550.0950.1590.2270.2850.1300.9590.2910.2490.1820.2370.2370.1251.0000.0000.0000.0000.8661.0000.1430.027
second0.008-0.002-0.0150.0360.041-0.001-0.0031.0000.0100.0000.3730.0930.1491.0001.0001.0000.4470.0000.0000.0000.1131.0000.0030.0000.1600.0320.0000.1030.0700.0840.1040.0820.1310.1310.0001.0000.0000.0000.4690.7071.0000.0670.000
shot_statsbomb_xg-0.290-0.150-0.145NaNNaN-0.039-0.1470.0101.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3040.3040.0001.0000.3380.5070.0000.0000.0000.3041.000
clearance_body_part1.0000.1860.1460.0000.0000.0000.1250.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3070.3070.0000.0000.0000.0000.0000.0000.0000.2671.000
dribble_outcome0.0000.0000.0000.0000.0000.0520.0000.3730.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
duel_outcome0.0000.0000.0000.0000.0000.0000.0000.0930.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.2610.0000.2180.2180.0000.0000.0000.0000.0000.0000.0000.0001.000
duel_type0.0000.1830.0880.0000.0000.0000.2430.1490.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6360.0000.0700.1250.1250.0000.0000.0000.0000.0000.0000.0000.0001.000
foul_committed_card1.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
foul_committed_type1.0000.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.000
goalkeeper_body_part1.0001.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.3330.0000.8160.0000.0000.0000.0000.0000.0000.0000.0000.0000.3330.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
goalkeeper_outcome1.0000.0000.0000.0000.0000.0000.0000.4470.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.3230.0000.0000.0000.0000.0000.0000.0000.0000.5240.4330.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
goalkeeper_position1.0000.1720.2390.0000.0000.2590.2780.0000.0000.0000.0000.0000.0000.0000.0000.3330.0001.0000.0000.3380.0000.0000.0000.0000.0000.0000.0000.0000.1570.0000.2591.0000.2590.2590.0000.0000.0000.0000.0000.0000.0000.2591.000
goalkeeper_technique1.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
goalkeeper_type1.0000.3220.1210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.8160.3230.3380.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4720.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
interception_outcome1.0000.0000.0000.0000.0000.0000.0000.1130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2900.3570.6350.6350.0000.0000.0000.0000.0000.0000.0000.2551.000
pass_assisted_shot_id1.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0001.0001.000
pass_body_part0.2960.0360.0580.0690.2690.1510.0550.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.2260.0000.2260.0000.0000.0440.0750.3680.3070.2040.2040.0000.0000.0000.0000.0000.0000.0000.2261.000
pass_height0.3080.1090.1010.1420.3730.1070.0950.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.2261.0000.0720.2620.1550.4900.0600.1380.2450.1820.1450.1450.0000.0000.0000.0000.0000.0000.0000.1571.000
pass_outcome0.1200.1330.0990.0000.1110.0000.1590.1600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0721.0000.1960.9050.0000.0900.0210.0000.1490.0000.0000.0000.0000.0000.0000.0000.0000.0000.1331.000
pass_recipient0.2150.2120.2190.2600.1740.2360.2270.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.2260.2620.1961.0000.5390.1020.4190.1500.2980.2690.9000.9000.0000.0000.0000.0000.0000.0000.0000.9851.000
pass_technique0.2120.0750.0710.5460.0000.0000.2850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.1550.9050.5391.0000.8450.0190.2890.4540.5040.3710.3710.0000.0000.0000.0000.0000.0000.0000.3781.000
pass_type0.2080.1090.0700.2320.2700.2020.1300.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.4900.0000.1020.8451.0000.2420.7720.5300.4830.0000.0000.0000.0000.0000.0000.0000.0000.0000.1481.000
period0.0580.9470.9300.0320.0310.1770.9590.0700.0000.0000.0000.0000.0000.0001.0000.0000.5240.1570.0000.0000.0001.0000.0440.0600.0900.4190.0190.2421.0000.3540.4450.4240.0100.0100.0001.0000.0000.0000.0000.3121.0000.0000.067
play_pattern0.0000.3010.2890.0610.0190.0750.2910.0840.0000.0000.0000.2300.6360.0000.0000.3330.4330.0000.0000.4720.0001.0000.0750.1380.0210.1500.2890.7720.3541.0000.1780.1240.3160.3160.0001.0000.0000.0000.5960.0001.0000.2020.060
player0.0560.2340.2450.1950.1880.9970.2490.1040.0000.0000.0000.2610.0000.0000.0000.0000.0000.2590.0000.0000.2901.0000.3680.2450.0000.2980.4540.5300.4450.1781.0000.9090.6380.6380.4721.0000.0000.2080.0001.0001.0000.9960.208
position0.0500.1770.1780.1710.1260.7640.1820.0820.0000.0000.0000.0000.0700.0000.0001.0001.0001.0001.0001.0000.3571.0000.3070.1820.1490.2690.5040.4830.4240.1240.9091.0000.5150.5150.4581.0000.0000.1610.4910.5001.0000.7490.200
possession_team0.0130.2150.1960.1110.1430.3070.2370.1310.3040.3070.0000.2180.1250.0000.0000.0000.0000.2590.0000.0000.6351.0000.2040.1450.0000.9000.3710.0000.0100.3160.6380.5151.0000.9990.2851.0000.0000.0000.0000.0001.0000.6260.085
possession_team_id0.0130.2150.1960.1110.1430.3070.2370.1310.3040.3070.0000.2180.1250.0000.0000.0000.0000.2590.0000.0000.6351.0000.2040.1450.0000.9000.3710.0000.0100.3160.6380.5150.9991.0000.2851.0000.0000.0000.0000.0001.0000.6260.085
shot_body_part0.0000.0000.1340.0000.0000.1690.1250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4720.4580.2850.2851.0001.0000.3260.0000.0000.0000.0000.2851.000
shot_key_pass_id1.0001.0001.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0001.000
shot_outcome0.3940.0820.1590.0000.0000.0000.0000.0000.3380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3261.0001.0000.0000.0000.0000.0000.0001.000
shot_technique0.0000.0000.2010.0000.0000.1450.0000.0000.5070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2080.1610.0000.0000.0001.0000.0001.0000.0150.0000.0000.0001.000
shot_type0.0000.1610.2440.0000.0000.0000.0000.4690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5960.0000.4910.0000.0000.0001.0000.0000.0151.0000.0000.0000.0001.000
substitution_outcome1.0000.8660.3540.0000.0000.0000.8660.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3120.0001.0000.5000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.000
substitution_replacement1.0001.0001.0000.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0001.0001.0001.0001.000
team0.0780.1160.0930.1460.1910.5020.1430.0670.3040.2670.0000.0000.0000.0001.0000.0000.0000.2590.0000.0000.2551.0000.2260.1570.1330.9850.3780.1480.0000.2020.9960.7490.6260.6260.2851.0000.0000.0000.0000.0001.0001.0000.216
type0.0820.0370.0251.0001.0000.0710.0270.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0670.0600.2080.2000.0850.0851.0001.0001.0001.0001.0001.0001.0000.2161.000

Missing values

2023-05-19T13:56:20.535523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-19T13:56:22.212552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-19T13:56:26.520281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

bad_behaviour_cardball_receipt_outcomeball_recovery_offensiveball_recovery_recovery_failureblock_deflectionblock_offensivecarry_end_locationclearance_aerial_wonclearance_body_partclearance_headclearance_left_footclearance_right_footcounterpressdribble_nutmegdribble_outcomedribble_overrunduel_outcomeduel_typedurationfoul_committed_advantagefoul_committed_cardfoul_committed_offensivefoul_committed_typefoul_won_advantagefoul_won_defensivegoalkeeper_body_partgoalkeeper_end_locationgoalkeeper_outcomegoalkeeper_positiongoalkeeper_techniquegoalkeeper_typeiddf_indexinterception_outcomelocationmatch_idminutemiscontrol_aerial_wonoff_cameraoutpass_aerial_wonpass_anglepass_assisted_shot_idpass_body_partpass_crosspass_deflectedpass_end_locationpass_goal_assistpass_heightpass_inswingingpass_lengthpass_miscommunicationpass_no_touchpass_outcomepass_outswingingpass_recipientpass_shot_assistpass_straightpass_switchpass_techniquepass_through_ballpass_typeperiodplay_patternplayerplayer_idpositionpossessionpossession_teampossession_team_idrelated_eventssecondshot_aerial_wonshot_body_partshot_deflectedshot_end_locationshot_first_timeshot_freeze_frameshot_key_pass_idshot_outcomeshot_statsbomb_xgshot_techniqueshot_typesubstitution_outcomesubstitution_replacementtacticsteamtimestamptypeunder_pressure
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